Computer Methods for Automatic Locomotion and Gesture Tracking in Mice and Small Animals for Neuroscience Applications: A Survey
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Neuroscience has traditionally relied on manually observing laboratory animals in controlled environments. Researchers usually record animals behaving freely or in a restrained manner and then annotate the data manually. The manual annotation is not desirable for three reasons; (i) it is time-consuming, (ii) it is prone to human errors, and (iii) no two human annotators will 100% agree on annotation, therefore, it is not reproducible. Consequently, automated annotation for such data has gained traction because it is efficient and replicable. Usually, the automatic annotation of neuroscience data relies on computer vision and machine learning techniques. In this article, we have covered most of the approaches taken by researchers for locomotion and gesture tracking of specific laboratory animals, i.e. rodents. We have divided these papers into categories based upon the hardware they use and the software approach they take. We have also summarized their strengths and weaknesses.
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Recent developments in behavioral analysis permit sophisticated examination of behavioral phenotypes based on unsupervised machine learning.
“Finally, recent developments in behavioral analysis [25][26][27] permit sophisticated examination of behavioral phenotypes based on unsupervised machine learning.”
From Projection-specific deficits in synaptic transmission in adult Sapap3-knockout mice (2020) · cites this paper as [27]
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You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,748 · see citing works
(1981) · Cited by 11,963 · see citing works
Mean shift: a robust approach toward feature space analysis
Comaniciu, D., & Meer, P. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000236 · Cited by 11,417 · see citing works
Horn, B. K. P., & Schunck, B. G. (1981) · Artificial Intelligence · doi:10.1016/0004-3702(81)90024-2 · Cited by 10,312 · see citing works
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.143 · Cited by 7,539 · cited 3× · see citing works
“For instance, Openpose can have a hard time identifying a pose it has not seen.”
MethodDeepLabCut: markerless pose estimation of user-defined body parts with deep learning
Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0209-y · Cited by 5,187 · see citing works
Yılmaz, A., Javed, O., & Shah, M. A. (2006) · ACM Computing Surveys · doi:10.1145/1177352.1177355 · Cited by 4,807 · see citing works
Performance of optical flow techniques
Barron, J. L., Fleet, D. J., & Beauchemin, S. S. (1994) · International Journal of Computer Vision · doi:10.1007/bf01420984 · Cited by 4,305 · see citing works
DeepPose: Human Pose Estimation via Deep Neural Networks
Toshev, A., & Szegedy, C. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.214 · Cited by 3,288 · see citing works
The use of a plus-maze to measure anxiety in the mouse
Lister, R. G. (1987) · Psychopharmacology · doi:10.1007/bf00177912 · Cited by 2,617 · see citing works
A Survey on Visual Surveillance of Object Motion and Behaviors
Hu, W., Tan, T. N., Wang, L., & Maybank, S. J. (2004) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2004.829274 · Cited by 2,132 · see citing works
Use of the Open Field Maze to Measure Locomotor and Anxiety-like Behavior in Mice
Seibenhener, M. L., & Wooten, M. C. (2015) · Journal of Visualized Experiments · doi:10.3791/52434 · Cited by 1,467 · see citing works
DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model
Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., & Schiele, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_3 · Cited by 1,191 · see citing works
Neurophysiology of locomotor automatism
Shik, M. L., & Orlovsky, G. N. (1976) · Physiological Reviews · doi:10.1152/physrev.1976.56.3.465 · Cited by 1,071 · see citing works
Geodesic active contours and level sets for the detection and tracking of moving objects
Paragios, N., & Deriche, R. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.841758 · Cited by 1,006 · see citing works
(2000) · Cited by 919 · see citing works
Learning a Deep Compact Image Representation for Visual Tracking
Wang, N., & Yeung, D. (2013) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 836 · see citing works
Arousal and Locomotion Make Distinct Contributions to Cortical Activity Patterns and Visual Encoding
Vinck, M., Batista-Brito, R., Knoblich, U., & Cardin, J. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.028 · Cited by 819 · see citing works
Crawley, J. N. (1999) · Brain Research · doi:10.1016/s0006-8993(98)01258-x · Cited by 762 · see citing works
A system for learning statistical motion patterns
Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., & Maybank, S. J. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.176 · Cited by 539 · see citing works
Motion tracking: no silver bullet, but a respectable arsenal
Welch, G., & Foxlin, E. M. (2002) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2002.1046626 · Cited by 537 · see citing works
Animal Models of Depression: Molecular Perspectives
Krishnan, V., & Nestler, E. J. (2011) · Current Topics in Behavioral Neurosciences · doi:10.1007/7854_2010_108 · Cited by 528 · see citing works
Principles of visual motion detection
Borst, A., & Egelhaaf, M. (1989) · Trends in Neurosciences · doi:10.1016/0166-2236(89)90010-6 · Cited by 522 · see citing works
Deep visual tracking: Review and experimental comparison
Li, P., Wang, D., Wang, L., & Lu, H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.11.007 · Cited by 517 · see citing works
Hamers, F. P. T., Lankhorst, A. J., van Laar, T. J., Veldhuis, W. B., & Gispen, W. H. (2001) · Journal of Neurotrauma · doi:10.1089/08977150150502613 · Cited by 500 · see citing works
What's Wrong With My Mouse?: Behavioral Phenotyping of Transgenic and Knockout Mice
Crawley, J. N. (2007) · doi:10.1002/0470119055 · Cited by 474 · see citing works
FABRIK: A fast, iterative solver for the Inverse Kinematics problem
Aristidou, A., & Lasenby, J. (2011) · Graphical Models · doi:10.1016/j.gmod.2011.05.003 · Cited by 428 · see citing works
The Gaits of Bipedal and Quadrupedal Animals
Alexander, R. M. (1984) · The International Journal of Robotics Research · doi:10.1177/027836498400300205 · Cited by 412 · see citing works
Automated monitoring and analysis of social behavior in Drosophila
Dankert, H., Wang, L., Hoopfer, E. D., Anderson, D. J., & Perona, P. (2009) · Nature Methods · doi:10.1038/nmeth.1310 · Cited by 382 · see citing works
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
Sun, S., Kuang, Z., Sheng, L., Ouyang, W., & Zhang, W. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00151 · Cited by 347 · see citing works
Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion
Dunn, T. W., Mu, Y., Narayan, S., Randlett, O., Naumann, E. A., Yang, C.-T., Schier, A. F., Freeman, J., Engert, F., & Ahrens, M. B. (2016) · eLife · doi:10.7554/elife.12741 · Cited by 312 · see citing works
Tashman, S., & Anderst, W. J. (2003) · Journal of Biomechanical Engineering · doi:10.1115/1.1559896 · Cited by 305 · see citing works
Vision and Locomotion Shape the Interactions between Neuron Types in Mouse Visual Cortex
Dipoppa, M., Ranson, A., Krumin, M., Pachitariu, M., Carandini, M., & Harris, K. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.037 · Cited by 280 · see citing works
Real-time identification and localization of body parts from depth images
Plagemann, C., Ganapathi, V., Koller, D., & Thrun, S. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509559 · Cited by 275 · see citing works
A hitchhiker's guide to behavioral analysis in laboratory rodents
Sousa, N., Almeida, O. F. X., & Wotjak, C. T. (2006) · Genes Brain & Behavior · doi:10.1111/j.1601-183x.2006.00228.x · Cited by 273 · see citing works
Tatem, K. S., Quinn, J. L., Phadke, A., Yu, Q., Gordish‐Dressman, H., & Nagaraju, K. (2014) · Journal of Visualized Experiments · doi:10.3791/51785 · Cited by 256 · see citing works
Detecting salient motion by accumulating directionally-consistent flow
Wixson, L. E. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868680 · Cited by 240 · see citing works
Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation
Rhodin, H., Salzmann, M., & Fua, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_46 · Cited by 218 · see citing works
Dysfunctional cerebellar Purkinje cells contribute to autism-like behaviour in Shank2-deficient mice
Peter, S., ten Brinke, M. M., Stedehouder, J., Reinelt, C. M., Wu, B., Zhou, H., Zhou, K., Boele, H., Kushner, S. A., Lee, M. G., et al. (2016) · Nature Communications · doi:10.1038/ncomms12627 · Cited by 217 · see citing works
Irschick, D. J., & Jayne, B. C. (1999) · Journal of Experimental Biology · doi:10.1242/jeb.202.9.1047 · Cited by 212 · see citing works
Locomotion Enhances Neural Encoding of Visual Stimuli in Mouse V1
Dadarlat, M. C., & Stryker, M. P. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.2728-16.2017 · Cited by 205 · see citing works
Unsupervised 3D Pose Estimation With Geometric Self-Supervision
Chen, C.-H., Tyagi, A., Agrawal, A., Drover, D., Rohith, M. V., Stojanov, S., & Rehg, J. M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00586 · Cited by 194 · see citing works
A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences
Asadi, M., Clapés, A., Bellantonio, M., Escalante, H. J., Ponce-López, V., Baró, X., Guyon, I., Shohreh Kasaei, B., & Escalera, S. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.150 · Cited by 176 · see citing works
Differential anxiogenic, aversive, and locomotor effects of THC in adolescent and adult rats
Schramm‐Sapyta, N. L., Cha, Y. M., Chaudhry, S., Wilson, W. A., Swartzwelder, H. S., & Kuhn, C. M. (2007) · Psychopharmacology · doi:10.1007/s00213-006-0676-9 · Cited by 172 · see citing works
Fasudil attenuates aggregation of α-synuclein in models of Parkinson’s disease
Tatenhorst, L., Eckermann, K., Dambeck, V., Fonseca-Ornelas, L., Walle, H., Lopes da Fonseca, T., Koch, J. C., Becker, S., Tönges, L., Bähr, M., et al. (2016) · Acta Neuropathologica Communications · doi:10.1186/s40478-016-0310-y · Cited by 158 · see citing works
Automated Tracking of Whiskers in Videos of Head Fixed Rodents
Clack, N. G., O’Connor, D. H., Huber, D., Petreanu, L., Hires, S. A., Peron, S., Svoboda, K., & Myers, E. W. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002591 · Cited by 147 · see citing works
Harvey, M. A. (2001) · Somatosensory & Motor Research · doi:10.1080/01421590120072204 · Cited by 145 · see citing works
Tracking Whisker and Head Movements in Unrestrained Behaving Rodents
Knutsen, P. M., Derdikman, D., & Ahissar, E. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00718.2004 · Cited by 140 · see citing works
Sepulveda-Falla, D., Barrera-Ocampo, A., Hagel, C., Korwitz, A., Vinueza-Veloz, M. F., Zhou, K., Schonewille, M., Zhou, H., Velazquez-Perez, L., Rodriguez-Labrada, R., et al. (2014) · Journal of Clinical Investigation · doi:10.1172/jci66407 · Cited by 135 · see citing works
Bootstrap prediction and confidence bands: a superior statistical method for analysis of gait data
Lenhoff, M., Santner, T. J., Otis, J. C., Peterson, M. G. E., Williams, B. J., & Backus, S. I. (1999) · Gait & Posture · doi:10.1016/s0966-6362(98)00043-5 · Cited by 130 · see citing works
Cerebellar control of gait and interlimb coordination
Veloz, M. F. V., Zhou, K., Bosman, L. W. J., Potters, J.-W., Negrello, M., Seepers, R. M., Strydis, C., Koekkoek, S. K. E., & Zeeuw, C. I. D. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0870-1 · Cited by 121 · see citing works
Quantification of gait parameters in freely walking rodents
Mendes, C. S., Bartos, I., Márka, Z., Akay, T., Szabolcs Marka, C., & Mann, R. S. (2015) · BMC Biology · doi:10.1186/s12915-015-0154-0 · Cited by 114 · see citing works
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation ModelPreprint
Insafutdinov, E., Pishchulin, L., Andres, B., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.03170 · Cited by 113 · see citing works
Optical Flow Based Moving Object Detection And Tracking For Traffic Surveillance
Aslani, S., & Mahdavi‐Nasab, H. (2013) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1088501 · Cited by 111 · see citing works
Turner, D. L., Ramos‐Murguialday, A., Birbaumer, N., Hoffmann, U., & Luft, A. R. (2013) · Frontiers in Neurology · doi:10.3389/fneur.2013.00184 · Cited by 110 · see citing works
Arac, A., Zhao, P., Dobkin, B. H., Carmichael, S. T., & Golshani, P. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2019.00020 · Cited by 108 · see citing works
Ha, S., Lee, D., Cho, Y. S., Chung, C., Yoo, Y.-E., Kim, J., Lee, J., Kim, W., Kim, H., Bae, Y. C., et al. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1849-16.2016 · Cited by 107 · see citing works
Gait Analysis in Normal and Spinal Contused Mice Using the TreadScan System
Beare, J. E., Morehouse, J. R., DeVries, W. H., Enzmann, G. U., Burke, D. A., Magnuson, D. S. K., & Whittemore, S. R. (2009) · Journal of Neurotrauma · doi:10.1089/neu.2009.0914 · Cited by 105 · see citing works
Optoelectronic monitoring of individual whisker movements in rats
Bermejo, R., Houben, D., & Zeigler, H. P. (1998) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(98)00050-8 · Cited by 104 · see citing works
Behavioral phenotyping of rodents.
Crawley, J. N. (2003) · PubMed · Cited by 102 · see citing works
Mouse behavioural analysis in systems biology
van Meer, P., & Raber, J. (2005) · Biochemical Journal · doi:10.1042/bj20042023 · Cited by 99 · see citing works
Unsupervised Whisker Tracking in Unrestrained Behaving Animals
Voigts, J., Sakmann, B. J., & Celikel, T. (2008) · Journal of Neurophysiology · doi:10.1152/jn.00012.2008 · Cited by 94 · see citing works
Locomotor and reinforcing effects of pentedrone, pentylone and methylone in rats
Paydar, M. J., Nguyen, J. D., Vandewater, S. A., Dickerson, T. J., & Taffe, M. A. (2017) · Neuropharmacology · doi:10.1016/j.neuropharm.2017.09.002 · Cited by 94 · see citing works
Leg-tracking and automated behavioural classification in Drosophila
Kain, J. S., Stokes, C. R., Gaudry, Q., Song, X., Foley, J., Wilson, R. I., & de Bivort, B. L. (2013) · Nature Communications · doi:10.1038/ncomms2908 · Cited by 92 · see citing works
Delayed decompression exacerbates ischemia-reperfusion injury in cervical compressive myelopathy.
Vidal, P., Karadimas, S., Ulndreaj, A., Laliberte, A. M., Tetreault, L., Forner, S., Wang, J., Foltz, W., & Fehlings, M. (2017) · JCI Insight · doi:10.1172/jci.insight.92512 · Cited by 90 · see citing works
Tracking with local spatio-temporal motion patterns in extremely crowded scenes
Kratz, L., & Nishino, K. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540149 · Cited by 89 · see citing works
Measuring anxiety- and locomotion-related behaviours in mice: a new way of using old tests
Fraser, L. M., Brown, R. E., Hussin, A. T., Fontana, M., Whittaker, A., O’Leary, T. P., Lederle, L. I., Holmes, A., & Ramos, A. (2010) · Psychopharmacology · doi:10.1007/s00213-010-1873-0 · Cited by 85 · see citing works
Reappraisal of Bergmann glial cells as modulators of cerebellar circuit function
Zeeuw, C. I. D., & Hoogland, T. M. (2015) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2015.00246 · Cited by 81 · see citing works
Real-time 3D motion tracking for small animal brain PET
Kyme, A. Z., Zhou, V., Meikle, S. R., & Fulton, R. (2008) · Physics in Medicine and Biology · doi:10.1088/0031-9155/53/10/014 · Cited by 81 · see citing works
Lemieux, M., Josset, N., Roussel, M., Couraud, S., & Bretzner, F. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00042 · Cited by 79 · see citing works
Locomotor therapy in neurorehabilitation
Hesse, S. (2001) · Neurorehabilitation · doi:10.3233/nre-2001-16302 · Cited by 76 · see citing works
Object Tracking and Detecting Based on Adaptive Background Subtraction
Zhang, R., & Ding, J. (2012) · Procedia Engineering · doi:10.1016/j.proeng.2012.01.139 · Cited by 76 · see citing works
Leech, K. A., Kinnaird, C. R., Holleran, C. L., Kahn, J. H., & Hornby, T. G. (2016) · Physical Therapy · doi:10.2522/ptj.20150646 · Cited by 73 · see citing works
Optimised Motion Tracking for Positron Emission Tomography Studies of Brain Function in Awake Rats
Kyme, A. Z., Zhou, V. W., Meikle, S. R., Baldock, C., & Fulton, R. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021727 · Cited by 72 · see citing works
An improved motion detection method for real-time surveillance
Lu, N., Wang, J., Wu, Q., & Yang, L. (2008) · Cited by 71 · see citing works
A Survey on Moving Object Tracking in Video
Deori, B., & Thounaojam, D. M. (2014) · International Journal on Information Theory · doi:10.5121/ijit.2014.3304 · Cited by 71 · see citing works
Evaluation of gait impairment in mice subjected to craniotomy and traumatic brain injury
Sashindranath, M., Daglas, M., & Medcalf, R. L. (2015) · Behavioural Brain Research · doi:10.1016/j.bbr.2015.02.038 · Cited by 68 · see citing works
Moving Object Detection Using Background Subtraction
Shaikh, S. H., Saeed, K., & Chaki, N. (2014) · SpringerBriefs in computer science · doi:10.1007/978-3-319-07386-6 · Cited by 68 · see citing works
Detecting independent motion: the statistics of temporal continuity
Pless, R. B., Brodský, T., & Aloimonos, Y. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868679 · Cited by 64 · see citing works
Robust Object Tracking Based on Temporal and Spatial Deep Networks
Zhu, T., Xing, J., Wang, Q., Lang, C., Feng, S., & Jin, Y. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.130 · Cited by 62 · see citing works
Gait analysis in three different 6-hydroxydopamine rat models of Parkinson's disease
Zhou, M., Zhang, W., Chang, J., Wang, J., Zheng, W., Yang, Y., Wen, P., Li, M., & Xiao, H. (2014) · Neuroscience Letters · doi:10.1016/j.neulet.2014.10.032 · Cited by 61 · see citing works
Adaptive detection and localization of moving objects in image sequences
Paragios, N., & Tziritas, G. (1999) · Signal Processing Image Communication · doi:10.1016/s0923-5965(98)00011-3 · Cited by 59 · see citing works
Krug, H. E., Dormán, C., Frizelle, S. P., Mahowald, M. L., & Funkenbusch, S. (2013) · Journal of Pain Research · doi:10.2147/jpr.s52195 · Cited by 57 · see citing works
Comparison of caffeine-induced locomotor activity between adolescent and adult rats
Marin, M. T., ZANCHETA, R. P., Paro, A. H., Possi, A. P. M., Cruz, F. C., & Planeta, C. S. (2011) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2011.03.052 · Cited by 55 · see citing works
Motion Human Detection Based on Background Subtraction
Zhang, L., & Liang, Y. (2010) · International Workshop on Education Technology and Computer Science · doi:10.1109/etcs.2010.440 · Cited by 55 · see citing works
Impact of genomics on research in the ratPreprint
Lazar, J., Moreno, C., Jacob, H. J., & Kwitek, A. E. (2005) · Genome Research · doi:10.1101/gr.3744005 · Cited by 53 · see citing works
Detection of subtle neurological alterations by the Catwalk XT gait analysis system
Chen, Y., Cheng, F., Sheu, M., Su, H.-L., Chen, C., Sheehan, J. P., & Pan, H.-C. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-62 · Cited by 53 · see citing works
Motion Detection Techniques Using Optical Flow
Shafie, A. A., Hafiz, F., & Ali, M. H. (2009) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1071465 · Cited by 51 · see citing works
Foreground motion detection by difference-based spatial temporal entropy image
Guo, J., Siong, C. E., & Rajan, D. (2004) · doi:10.1109/tencon.2004.1414436 · Cited by 51 · see citing works
Yang, T.-H. O., Tsai, M., Yen, C., & Lin, T. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.07.019 · Cited by 50 · see citing works
Yun, S., Donovan, M. H., Ross, M. N., Richardson, D. R., Reister, R., Farnbauch, L. A., Fischer, S. J., Riethmacher, D., Gershenfeld, H. K., Lagace, D. C., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0147256 · Cited by 50 · see citing works
Bérillon, G., Daver, G., D’Août, K., Nicolas, G., de la Villetanet, B., Multon, F., Digrandi, G., & Dubreuil, G. (2010) · International Journal of Primatology · doi:10.1007/s10764-010-9398-2 · Cited by 49 · see citing works
Hou, J., Nelson, R., Nissim, N. R., Parmer, R., Thompson, F. J., & Bose, P. (2014) · Journal of Neurotrauma · doi:10.1089/neu.2013.3096 · Cited by 48 · see citing works
Liu, Y. Z., Ao, L. J., Lü, G., Leong, E. Y. M., Liu, Q., Wang, X. H., Zhu, X. L., Sun, T. F. D., Fei, Z., Jiu, T., et al. (2013) · Behavioural Brain Research · doi:10.1016/j.bbr.2013.10.007 · Cited by 47 · see citing works
Unsupervised Gesture Segmentation by Motion Detection of a Real-Time Data Stream
Simao, M. A., Neto, P., & Gibaru, O. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2613683 · Cited by 46 · see citing works
Multiple human object tracking using background subtraction and shadow removal techniques
Saravanakumar, S., Vadivel, A., & Ahmed, C. G. S. (2010) · doi:10.1109/icsip.2010.5697446 · Cited by 45 · see citing works
Motion Detection Based on Temporal Difference Method and Optical Flow field
Shuigen, W., Chen, Z., & Hua, D. (2009) · International Symposium on Electronic Commerce and Security · doi:10.1109/isecs.2009.62 · Cited by 44 · see citing works
Weakly supervised object detection with 2D and 3D regression neural networks
Dubost, F., Adams, H. H. H., Yilmaz, P., Bortsova, G., van Tulder, G., Ikram, M. A., Niessen, W. J., Vernooij, M. W., & de Bruijne, M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101767 · Cited by 44 · see citing works
Synchronization of Motor Neurons during Locomotion in the Neonatal Rat: Predictors and Mechanisms
Tresch, M. C., & Kiehn, O. (2002) · Journal of Neuroscience · doi:10.1523/jneurosci.22-22-09997.2002 · Cited by 43 · see citing works
Wireless inertial measurement of head kinematics in freely-moving rats
Pasquet, M. O., Tihy, M., Gourgeon, A., Pompili, M. N., Godsil, B. P., Léna, C., & Dugué, G. P. (2016) · Scientific Reports · doi:10.1038/srep35689 · Cited by 38 · see citing works
Walker, Q. D., Schramm‐Sapyta, N. L., Caster, J. M., Waller, S. T., Brooks, M. P., & Kuhn, C. M. (2008) · Pharmacology Biochemistry and Behavior · doi:10.1016/j.pbb.2008.08.019 · Cited by 36 · see citing works
Automated video analysis system reveals distinct diurnal behaviors in C57BL/6 and C3H/HeN mice
Adamah‐Biassi, E. B., Stepien, I., Hudson, R. L., & Dubocovich, M. L. (2013) · Behavioural Brain Research · doi:10.1016/j.bbr.2013.01.003 · Cited by 35 · see citing works
Computer-Assisted 3D Kinematic Analysis of All Leg Joints in Walking Insects
Bender, J. A., Simpson, E. M., & Ritzmann, R. E. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013617 · Cited by 35 · see citing works
Changes in feeding and locomotion induced by amphetamine analogs in rats
Wellman, P. J., Davis, K. W., Clifford, P. S., Rothman, R. B., & Blough, B. E. (2008) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2008.10.005 · Cited by 34 · see citing works
Hampton, T., & Amende, I. (2010) · Journal of Motor Behavior · doi:10.1080/00222890903272025 · Cited by 34 · see citing works
Kyzar, E. J., Gaikwad, S., Roth, A. J. L., Green, J. B. A., Pham, M., Stewart, A., Liang, Y., Kobla, V., & Kalueff, A. V. (2011) · Behavioural Brain Research · doi:10.1016/j.bbr.2011.07.052 · Cited by 34 · see citing works
Nashaat, M. A., Oraby, H., Peña, L. B., Dominiak, S. E., Larkum, M. E., & Sachdev, R. N. S. (2017) · eNeuro · doi:10.1523/eneuro.0245-16.2017 · Cited by 34 · see citing works
Low-cost three-dimensional gait analysis system for mice with an infrared depth sensor
Nakamura, A., Funaya, H., Uezono, N., Nakashima, K., Ishida, Y., Suzuki, T., Wakana, S., & Shibata, T. (2015) · Neuroscience Research · doi:10.1016/j.neures.2015.06.006 · Cited by 33 · see citing works
Roy, S., Bryant, J. L., Cao, Y., & Heck, D. H. (2011) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2011.00027 · Cited by 32 · see citing works
Kloefkorn, H. E., Pettengill, T. R., Turner, S. M. F., Streeter, K., Gonzalez‐Rothi, E. J., Fuller, D. D., & Allen, K. D. (2016) · Annals of Biomedical Engineering · doi:10.1007/s10439-016-1717-0 · Cited by 30 · see citing works
Automatic system for analysis of locomotor activity in rodents—A reproducibility study
Aragão, R. D. S., Rodrigues, M. A. B., Barros, K. M. F. T., Silva, S. R. F., Toscano, A. E., de Souza, R. E., & Manhães‐de‐Castro, R. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.12.016 · Cited by 28 · see citing works
Neurobehavioral deficits in the KIKO mouse model of Friedreich’s ataxia
McMackin, M. Z., Henderson, C. K., & Cortopassi, G. A. (2016) · Behavioural Brain Research · doi:10.1016/j.bbr.2016.08.053 · Cited by 28 · see citing works
Kyzar, E., Pham, M., Roth, A., Jonathan Cachat, P., Green, J., Gaikwad, S., & Kalueff, A. (2012) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2012.08.004 · Cited by 26 · see citing works
Towards a kinect-based behavior recognition and analysis system for small animals
Wang, Z., Mirbozorgi, S. A., & Ghovanloo, M. (2015) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2015.7348456 · Cited by 25 · see citing works
Connell, J. W., Allison, R. J., & Reid, E. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152413 · Cited by 25 · see citing works
Unsupervised learning of human expressions, gestures, and actions
O′Hara, S., Lui, Y. M., & Draper, B. A. (2011) · doi:10.1109/fg.2011.5771473 · Cited by 24 · see citing works
Locomotor activity: A distinctive index in morphine self-administration in rats
Zhang, J., & Kong, Q. (2017) · PLoS ONE · doi:10.1371/journal.pone.0174272 · Cited by 24 · see citing works
Neckel, N. D. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.06.017 · Cited by 23 · see citing works
Lambert, C. S., Philpot, R. M., Engberg, M. E., Johns, B., Kim, S. H., & Wecker, L. (2014) · Behavioural Brain Research · doi:10.1016/j.bbr.2014.08.004 · Cited by 21 · see citing works
Gellhaar, S., Marcellino, D. J., Abrams, M. B., & Galter, D. (2015) · Genes Brain & Behavior · doi:10.1111/gbb.12210 · Cited by 20 · see citing works
In Vivo Tracing of Neural Tracts in Tiptoe Walking Yoshimura Mice by Diffusion Tensor Tractography
Takano, M., Komaki, Y., Hikishima, K., Konomi, T., Fujiyoshi, K., Tsuji, O., Toyama, Y., Okano, H., & Nakamura, M. (2012) · Spine · doi:10.1097/brs.0b013e31827aacc2 · Cited by 18 · see citing works
Wiechman, B. (1981) · Pharmacology Biochemistry and Behavior · doi:10.1016/0091-3057(81)90273-2 · Cited by 18 · see citing works
Automated gesture tracking in head-fixed mice
Giovannucci, A., Pnevmatikakis, E. A., Deverett, B., Pereira, T., Fondriest, J., Brady, M. J., Wang, S., Abbas, W., Parés, P., & Masip, D. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.07.014 · Cited by 18 · see citing works
Self-citationTracking and characterizing the head motion of unanaesthetized rats in positron emission tomography
Kyme, A. Z., Meikle, S. R., Baldock, C., & Fulton, R. (2012) · Journal of The Royal Society Interface · doi:10.1098/rsif.2012.0334 · Cited by 17 · see citing works
Motor phenotypes and molecular networks associated with germline deficiency of Ciz1
Xiao, J., Vemula, S. R., Xue, Y., Khan, M. M., Kuruvilla, K. P., Marquez-Lona, E. M., Cobb, M. R., & LeDoux, M. S. (2016) · Experimental Neurology · doi:10.1016/j.expneurol.2016.05.006 · Cited by 17 · see citing works
Nascimento-Castro, C. D. P., Wink, A. C., da Fonsêca, V. S., Bianco, C. D., Duarte, E. C. W., Farina, M., Rodrigues, A. L. S., Gil‐Mohapel, J., Bem, A. F. D., & Brocardo, P. S. (2018) · Neural Plasticity · doi:10.1155/2018/4056383 · Cited by 17 · see citing works
Unsupervised 3D Pose Estimation with Geometric Self-SupervisionPreprint
Chen, C.-H., Tyagi, A., Agrawal, A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04812 · Cited by 17 · see citing works
A system for automated tracking of motor components in neurophysiological research
Palmér, T., Tamté, M., Halje, P., Enqvist, O., & Petersson, P. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.01.008 · Cited by 16 · see citing works
Sleep pattern and locomotor activity are impaired by doxorubicin in non-tumor-bearing rats
Lira, F. S., Esteves, A. M., Pimentel, G. D., Neto, J. C. R., Frank, M. K., Mariano, M. O., Budni, J., Quevedo, J., dos Santos, R. V. T., & de Mello, M. T. (2016) · Sleep Science · doi:10.1016/j.slsci.2016.10.006 · Cited by 16 · see citing works
Gravel, P., Tremblay, M., Leblond, H., Rossignol, S., & de Guise, J. A. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.05.006 · Cited by 15 · see citing works
Learning Spatio-Temporal Information for Multi-Object Tracking
Wei, J., Yang, M., & Liu, F. (2017) · IEEE Access · doi:10.1109/access.2017.2686482 · Cited by 15 · see citing works
The effect on ephedrine prodrugs on locomotor activity in rats
Walker, R. B., Fitz, L. D., Williams, L. M., & McDaniel, Y. M. (1996) · General Pharmacology The Vascular System · doi:10.1016/0306-3623(95)00127-1 · Cited by 14 · see citing works
Detailed tracking of body and leg movements of a freely walking female cricket during phonotaxis
Πέτρου, Γ., & Webb, B. H. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.09.011 · Cited by 14 · see citing works
Automatic analysis of altered gait in arylsulphatase A-deficient mice in the open field
Leroy, T., Stroobants, S., Aerts, J., D’Hooge, R., & Berckmans, D. (2009) · Behavior Research Methods · doi:10.3758/brm.41.3.787 · Cited by 12 · see citing works
A depth-map approach for automatic mice behavior recognition
Monteiro, J., Oliveira, H. P., Aguiar, P., & Cardoso, J. S. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025458 · Cited by 11 · see citing works
Statistical Methods and Models for Video-Based Tracking, Modeling, and Recognition
Chellappa, R. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601983152 · Cited by 11 · see citing works
A Review of Visual Tracking with Deep Learning
Feng, X., Mei, W., & Hu, D. (2016) · doi:10.2991/aiie-16.2016.54 · Cited by 11 · see citing works
Statistical Methods and Models for Video-Based Tracking, Modeling, and Recognition
Chellappa, R., Sankaranarayanan, A. C., Veeraraghavan, A., & Turaga, P. K. (2010) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000007 · Cited by 11 · see citing works
Abed, A. E., Dubuisson, S., & Béréziat, D. (2006) · Lecture notes in computer science · doi:10.1007/11864349_17 · Cited by 10 · see citing works
An intelligent surveillance system based on motion detection
Ge, R., Shan, Z., & Kou, H. (2011) · IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT) · doi:10.1109/icbnmt.2011.6155946 · Cited by 10 · see citing works
A Video Tracking System for Limb Motion Measurement in Small Animals
Xu, Q., Cong, C., Zhou, H., & Ren, H. (2010) · International Conference on Optoelectronics and Image Processing · doi:10.1109/icoip.2010.327 · Cited by 10 · see citing works
Statistical approach to classification of flow patterns for motion detection
Denzler, J., Schleß, V., Paulus, D. W. R., & Niemann, H. J. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.1996.559547 · Cited by 10 · see citing works
Fast, Flexible Closed-Loop Feedback: Tracking Movement in “Real-Millisecond-Time”
Sehara, K., Bahr, V., Mitchinson, B., Pearson, M. J., Larkum, M. E., & Sachdev, R. N. S. (2019) · eNeuro · doi:10.1523/eneuro.0147-19.2019 · Cited by 10 · see citing works
Dirks, A., Groenink, L., Verdouw, M. P., Schipholt, M. L., van der Gugten, J., Hijzen, T. H., & Olivier, B. (2001) · International Journal of Comparative Psychology · doi:10.46867/c44s3t · Cited by 9 · see citing works
Kyzar, E., Pham, M., Roth, A., Jonathan Cachat, P., Green, J., Gaikwad, S., & Kalueff, A. (2012) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2015.08.002 · Cited by 8 · see citing works
Shimogawa, T., Sakaguchi, H., Kikuchi, T., Tsuchimochi, R., Sano, N., Torikoshi, S., Ito, A., Aoyama, T., Iihara, K., & Takahashi, J. B. (2019) · npj Regenerative Medicine · doi:10.1038/s41536-019-0075-6 · Cited by 8 · see citing works
Background subtraction-based multiple object tracking using particle filter
Kim, I., Awan, T. W., & Soh, Y. (2014) · International Conference on Systems, Signals and Image Processing · Cited by 6 · see citing works
Tracking the joints of arthropod legs using multiple images and inverse kinematics
Hwang, S. S., & Choi, Y. K. (2015) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-015-0089-y · Cited by 6 · see citing works
Adamah‐Biassi, E. B., Stepien, I., Hudson, R. L., & Dubocovich, M. L. (2012) · The FASEB Journal · doi:10.1096/fasebj.26.1_supplement.1042.5 · Cited by 3 · see citing works
Motion tracking with fast adaptive background subtraction
Xiao, D., Yu, S., & Jingli, Z. (2003) · Wuhan University Journal of Natural Sciences · doi:10.1007/bf02902061 · Cited by 3 · see citing works
Rat Paw Tracking for Detailed Motion Analysis
Palmér, T., Åström, K., Enqvist, O., Ivica, N., & Petersson, P. (2014) · Lund University Publications (Lund University) · Cited by 3 · see citing works
Detecting Cerebellar Phenotypes with the Erasmus Ladder
Cupido, A. (2009) · RePub (Erasmus University Rotterdam) · Cited by 3 · see citing works
Locomotor programs versus ‘conventional’ physical therapy? Locomotor training
Wernig, A. (2012) · Spinal Cord · doi:10.1038/sc.2011.190 · Cited by 3 · see citing works
An algorithm for automatic tracking of rat whiskers
Gyory, G., Rankov, V., Gordon, G., Perkon, I., Mitchinson, B., Grant, R., & Prescott, T. (2015) · Repository of the University of Ljubljana (University of Ljubljana) · Cited by 3 · see citing works
In Vivo Tracing of Neural Tracts in Tiptoe-Walking Yoshimura Mice by Diffusion Tensor Tractography
Takano, M., Komaki, Y., Hikishima, K., Konomi, T., Fujiyoshi, K., Tsuji, O., Okano, H., Toyama, Y., & Nakamura, M. (2014) · Springer eBooks · doi:10.1007/978-4-431-54502-6_10 · Cited by 1 · see citing works
Semi-Automated Tracking Of Vibrissal Movements In Free-Moving Rodents Captured By High-Speed Videos
Kim, H. J., Shi, T., Akdaglı, S., Most, S. P., & Yan, Y. (2015) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1106864 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [10]Tekalp A.M. Tekalp A.M. Digital Video Processing Prentice Hall PTR Upper Saddle River, NJ, USA 1995 Volume 1
- [11]Bovik A.C. Handbook of Image and Video Processing Academic Press Cambridge, MA, USA 2010
- [32]Isard M. Visual Motion Analysis by Probabilistic Propagation of Conditional Density Ph.D. Thesis University of Oxford Oxford, UK 1998
- [111]Bishop C.M. Pattern Recognition and Machine Learning Springer New York, NY, USA 2007 738
- [155]Lala D. Mohammad Y. Nishida T. Unsupervised gesture recognition system for learning manipulative actions in virtual basketball Proceedings of the 1st International Conference on Human-Agent Interaction Sapporo, Japan 7–9 August 2013
Cited by
Sorted by citations
Heinzel, J. C., Laengle, G., Oberhauser, V., Hausner, T., Kolbenschlag, J., Prahm, C., Grillari, J., & Hercher, D. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108889 · Cited by 50 · see citing works
Projection-specific deficits in synaptic transmission in adult Sapap3-knockout mice
Hadjas, L. C., Schartner, M. M., Cand, J., Creed, M. C., Pascoli, V., Lüscher, C., & Simmler, L. D. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0747-3 · Cited by 46 · see citing works
Rodrigues, J., Studer, E., Streuber, S., Meyer, N. H., & Sandi, C. (2020) · Nature Communications · doi:10.1038/s41467-020-19736-3 · Cited by 31 · see citing works
Arent, I., Schmidt, F. P., Botsch, M., & Dürr, V. (2021) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2021.637806 · Cited by 13 · see citing works
Heinzel, J. C., Swiadek, N. V., Ashmwe, M., Rührnößl, A., Oberhauser, V., Kolbenschlag, J., & Hercher, D. (2020) · Journal of Visualized Experiments · doi:10.3791/61852 · Cited by 13 · see citing works
Shortwave infrared fluorescence imaging of peripheral organs in awake and freely moving mice
Arús, B. A., Cosco, E. D., Yiu, J., Balba, I., Bischof, T. S., Sletten, E. M., & Bruns†, O. T. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1135494 · Cited by 12 · see citing works
A new method for vibration-based neurophenotyping of zebrafish
Wang, J., Wang, D., Hu, G., Yang, L., Yan, D., Wang, M., Serikuly, N., Alpyshov, E. T., Amstislavskaya, T. G., Demin, K. A., et al. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108563 · Cited by 12 · see citing works
Fast, Flexible Closed-Loop Feedback: Tracking Movement in “Real-Millisecond-Time”
Sehara, K., Bahr, V., Mitchinson, B., Pearson, M. J., Larkum, M. E., & Sachdev, R. N. S. (2019) · eNeuro · doi:10.1523/eneuro.0147-19.2019 · Cited by 10 · see citing works
Automated Behavior Analysis Using a YOLO-Based Object Detection System
Amino, K., & Matsuo, T. (2022) · Neuromethods · doi:10.1007/978-1-0716-2321-3_14 · Cited by 9 · see citing works
Saha, R., Jiang, L., Salsabili, H., Faezipour, M., Ostadabbas, S., Larimer, B. M., & Mirbozorgi, S. A. (2023) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2023.3284823 · Cited by 7 · see citing works
Gait analysis in chemotherapy-induced peripheral neurotoxicity rodent models
López-Garzón, M., Canta, A., Chiorazzi, A., & Alberti, P. (2023) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2023.110769 · Cited by 5 · see citing works
Abbas, W., Masip, D., & Giovannucci, A. (2020) · IEEE Access · doi:10.1109/access.2020.2975926 · Cited by 5 · see citing works
Self-citationDiaz-Arce, D., Ghouma, A., Scalmani, P., Mantegazza, M., & Duprat, F. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.15.488472 · Cited by 5 · see citing works
High-throughput gait acquisition system for freely moving mice
Molina, L. A., Milla-Cruz, J. J., Ghavasieh, Z., Kim, L. H., Cheng, N., & Whelan, P. J. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00133.2023 · Cited by 3 · see citing works
Lam, D., Lindemann, M., Yang, K., Liu, D. X., Ludwig, K. A., & Shoffstall, A. J. (2023) · eNeuro · doi:10.1523/eneuro.0305-23.2023 · Cited by 2 · see citing works
Odrinskaya, М. S., Strelkova, E. V., Rebik, A., Aleksandrov, P. L., & Midzyanovskaya, I. S. (2026) · Biomechanics · doi:10.3390/biomechanics6020032 · Cited by 1 · see citing works
Automatization of Analysis Behavior in Crayfish
Sepúlveda-Hernández, M., Hernández‐Falcón, J., & Mendoza‐Ángeles, K. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-82123-3_26
Parra, D. L., Coronado, J. D. G., Jensen, G., Diaz, V. G., & Cárdenas, F. P. (2026) · PLoS ONE · doi:10.1371/journal.pone.0337381
- Authors
- Abbas, W., & Masip Rodo, D.
- Affiliations
- Universitat Oberta de Catalunya (ES)
- Multimedia and Telecommunications Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
- Corresponding author
- Abbas, Waseem
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-07-25
- Volume
- 19
- Issue
- 15
- Pages
- 3274
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6696321
- OpenAlex
- W2964155858
- Cited by
- 21
- References
- 156
- Citations by year
- 2026: 2 · 2025: 2 · 2023: 6 · 2022: 2 · 2021: 1 · 2020: 6 · 2019: 2
- FWCI
- 0.29
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministerio de Economía, Industria y Competitividad, Gobierno de España (TIN2015-66951-C2-2-R, RTI2018-095232-B-C22)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Keywords
- locomotion tracking, gesture tracking, mouse tracking, rodent behavior, computer vision, machine learning
- MeSH terms
- Machine Learning, Algorithms, Animals, Behavior, Animal, Gestures, Image Processing, Computer-Assisted, Locomotion, Neurosciences, Surveys and Questionnaires, Software, Mice
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