The DOI matches its Crossref record; 35 of the 36 references it cites were found in a registry or an index.
A Trainable System for Object Detection
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“At early times of object detection, NMS was not always integrated [121].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [121]
Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
A theory for multiresolution signal decomposition: the wavelet representation
Mallat, S. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.192463 · Cited by 21,463
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,616
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Christof Koch, A., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,289
Pfinder: real-time tracking of the human body
Wren, C. R., Azarbayejani, A. J., Darrell, T., & Pentland, A. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598236 · Cited by 4,169
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
Training support vector machines: an application to face detection
Osuna, E., Freund, R. M., & Girosi, F. (2002) · 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.1997.609310 · Cited by 2,510
Example-based learning for view-based human face detection
Sung, K.-K., & Poggio, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655648 · Cited by 1,784
Self-citationWavelets for computer graphics: a primer.1
Stollnitz, E. J., DeRose, A. D., & Salesin, D. (1995) · IEEE Computer Graphics and Applications · doi:10.1109/38.376616 · Cited by 742
Flagged first, then by citations
1 of the 36 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 101–200 of 1,000
ML Based Sign Language Recognition System
Amrutha, K., & Prabu, P. (2021) · International Conference on Innovative Trends in Information Technology (ICITIIT) · doi:10.1109/icitiit51526.2021.9399594 · Cited by 133
Sensor-based pedestrian protection
Gavrila, D. M. (2001) · IEEE Intelligent Systems · doi:10.1109/5254.972097 · Cited by 132
Prediction of RNA-binding proteins from primary sequence by a support vector machine approach
Han, L., Cai, C., Lo, S. L., Chung, M. C. M., & Chen, Y. (2004) · RNA · doi:10.1261/rna.5890304 · Cited by 132
Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features
Paisitkriangkrai, S., Shen, C., & Zhang, J. (2008) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2008.928213 · Cited by 131
Grauman, K., & Leibe, B. (2011) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00332ed1v01y201103aim011 · Cited by 126
Real-time Accurate Object Detection using Multiple Resolutions
Zhang, W., Zelinsky, G. J., & Samaras, D. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409057 · Cited by 126
Automatic Vehicle Detection Using Local Features—A Statistical Approach
Wang, C.-C. R., & Lien, J.-J. J. (2008) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2007.908572 · Cited by 124
Integrating representative and discriminant models for object category detection
Fritz, M., Leibe, B., Barbara Caputo, B., & Schiele, B. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.124 · Cited by 124
Pedestrian collision avoidance systems: a survey of computer vision based recent studies
Gandhi, T., & Trivedi, M. M. (2006) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2006.1706871 · Cited by 124
Shape-based pedestrian detection and tracking
Gavrila, D. M., & Giebel, J. (2003) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2002.1187920 · Cited by 122
A Cascade of Boosted Generative and Discriminative Classifiers for Vehicle Detection
Negri, P., Clady, X., Hanif, M. S., & Prévost, L. (2008) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2008/782432 · Cited by 120
Database-Guided Segmentation of Anatomical Structures with Complex Appearance
Georgescu, B., Zhou, X. S., Comaniciu, D., & Gupta, A. K. (2005) · 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.2005.119 · Cited by 120
Visual Recognition Using Local Quantized Patterns
Hussain, S. U., & Triggs, B. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_51 · Cited by 119
Pedestrian detection by means of far-infrared stereo vision
Bertozzi, M., Broggi, A., Caraffi, C., Rose, M. D., Felisa, M., & Vezzoni, G. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.07.016 · Cited by 119
Categorization by Learning and Combining Object Parts
Heisele, B., Serre, T., Pontil, M., Vetter, T. R., & Poggio, T. (2001) · Cited by 118
Self-citationKakaletsis, E., Symeonidis, C., Tzelepi, M., Mademlis, I., Tefas, A., Nikolaidis, N., & Pitas, I. (2021) · ACM Computing Surveys · doi:10.1145/3472288 · Cited by 117
Sliding-Windows for Rapid Object Class Localization: A Parallel Technique
Wojek, C., Dorkó, G., Schulz, A., & Schiele, B. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69321-5_8 · Cited by 117
A new pedestrian dataset for supervised learning
Overett, G., Petersson, L., Brewer, N., Andersson, L. A. A., & Pettersson, N. (2008) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2008.4621297 · Cited by 115
A real-time precrash vehicle detection system
Sun, Z., Miller, R. H., Bebis, G. N., & DiMeo, D. (2003) · Proceedings · doi:10.1109/acv.2002.1182177 · Cited by 115
An Operational System for Estimating Road Traffic Information from Aerial Images
Leitloff, J., Rosenbaum, D., Kurz, F., Meynberg, O., & Reinartz, P. H. (2014) · Remote Sensing · doi:10.3390/rs61111315 · Cited by 113
Building kernels from binary strings for image matching
Odone, F., Barla, A., & Verri, A. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.840701 · Cited by 113
Son, H., Kim, C., & Kim, C. (2011) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000141 · Cited by 112
Object Class Recognition Using Discriminative Local Features
Dorkó, G., & Schmid, C. (2005) · HAL · Cited by 110
Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages
Chen, Y., & Chen, C.-S. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.926152 · Cited by 107
Attacking vision-based perception in end-to-end autonomous driving models
Boloor, A., Garimella, K., He, X. J., Gill, C. D., Vorobeychik, Y., & Zhang, X. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101766 · Cited by 105
Measurement of glomerulus diameter and Bowman's space width of renal albino rats
Kotyk, T., Dey, N., Ashour, A. S., Rad, D., Chakraborty, S., Ahmed, A. S., & Tavares, J. M. R. S. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.10.023 · Cited by 104
Target Detection Method of UAV Aerial Imagery Based on Improved YOLOv5
Luo, X., Wu, Y., & Wang, F. (2022) · Remote Sensing · doi:10.3390/rs14195063 · Cited by 103
Mitra, T., Hutto, C. J., & Gilbert, E. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702553 · Cited by 102
Object recognition datasets and challenges: A review
Salari, A., Djavadifar, A., Liu, X., & Najjaran, H. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.022 · Cited by 102
Visual classification of coarse vehicle orientation using Histogram of Oriented Gradients features
Rybski, P. E., Huber, D., Morris, D., & Hoffman, R. (2010) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2010.5547996 · Cited by 101
An Efficient Hardware Implementation of HOG Feature Extraction for Human Detection
Chen, P., Huang, C.-C., Lien, C., & Tsai, Y.-H. (2013) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2013.2284666 · Cited by 99
Pedestrian Detection and Tracking Using a Mixture of View-Based Shape–Texture Models
Munder, S., Schnörr, C., & Gavrila, D. M. (2008) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2008.922943 · Cited by 98
Prioletti, A., Møgelmose, A., Grisleri, P., Trivedi, M. M., Broggi, A., & Moeslund, T. B. (2013) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2013.2262045 · Cited by 96
Open Vocabulary Object Detection with Pseudo Bounding-Box Labels
Gao, M., Chen, X., Niebles, J. C., Li, J., Xu, R., Liu, W., & Xiong, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_16 · Cited by 95
Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image
Kato, T., Relator, R., Ngouv, H., Hirohashi, Y., Takaki, O., Kakimoto, T., & Okada, K. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0739-1 · Cited by 94
Jin, X., & Davis, C. H. (2006) · Image and Vision Computing · doi:10.1016/j.imavis.2006.12.011 · Cited by 94
An Implicit Shape Model for Combined Object Categorization and Segmentation
Leibe, B., Leonardis, A., & Schiele, B. (2006) · Lecture notes in computer science · doi:10.1007/11957959_26 · Cited by 92
ViDT: An Efficient and Effective Fully Transformer-based Object DetectorPreprint
Song, H., Deqing, S., Chun, S., Jampani, V., Han, D., Heo, B., Kim, W., & Yang, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.03921 · Cited by 92
Robust CoHOG Feature Extraction in Human-Centered Image/Video Management System
Pang, Y., Yan, H., Yuan, Y., & Wang, K. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2167750 · Cited by 90
Pedestrian detection in far infrared images
Olmeda, D., Premebida, C., Nunes, U., Armingol, J. M., & de la Escalera, A. (2013) · Integrated Computer-Aided Engineering · doi:10.3233/ica-130441 · Cited by 89
Discriminative Feature Co-Occurrence Selection for Object Detection
Takahiro, M. I. T. A., Kaneko, T., Stenger, B., & Hori, O. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70767 · Cited by 89
The Benefits of Dense Stereo for Pedestrian Detection
Keller, C. G., Enzweiler, M., Rohrbach, M., Llorca, D. F., Schnörr, C., & Gavrila, D. M. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2143410 · Cited by 88
The adaptability and challenges of autonomous vehicles to pedestrians in urban China
Wang, K., Li, G., Chen, J., Yan, L., Chen, T., Chen, L., & Xia, Q. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105692 · Cited by 87
Attribute-Aware Pedestrian Detection in a Crowd
Zhang, J., Lin, L., Zhu, J., Li, Y., Chen, Y.-C., Hu, Y. G., & Hoi, S. C. H. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3020691 · Cited by 85
Integrated pedestrian classification and orientation estimation
Enzweiler, M., & Gavrila, D. M. (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.2010.5540110 · Cited by 84
A Parallel Hardware Architecture for Real-Time Object Detection with Support Vector Machines
Kyrkou, C., & Theocharides, T. (2011) · IEEE Transactions on Computers · doi:10.1109/tc.2011.113 · Cited by 84
Deep learning with RGB and thermal images onboard a drone for monitoring operations
Speth, S., Gonçalves, A., Rigault, B., Suzuki, S., Bouazizi, M., Matsuo, Y., & Prendinger, H. (2022) · Journal of Field Robotics · doi:10.1002/rob.22082 · Cited by 83
STDnet: Exploiting high resolution feature maps for small object detection
Bosquet, B., Mucientes, M., & Brea, V. M. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103615 · Cited by 80
A Novel Vehicle Detection Method With High Resolution Highway Aerial Image
Zheng, Z., Zhou, G., Wang, Y., Liu, Y., Li, X., Wang, X., & Jiang, L. L. (2013) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2013.2266131 · Cited by 80
Multi-Aspect Detection of Articulated Objects
Seemann, E., Leibe, B., & Schiele, B. (2006) · 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.2006.193 · Cited by 80
Learning a Family of Detectors via Multiplicative Kernels
Yuan, Q., Thangali, A., Ablavsky, V., & Sclaroff, S. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.117 · Cited by 79
A mixed generative-discriminative framework for pedestrian classification
Enzweiler, M., & Gavrila, D. M. (2008) · 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.2008.4587592 · Cited by 78
A systematic review of object detection from images using deep learning
Kaur, J., & Singh, W. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15981-y · Cited by 78
Mass Image Synthesis in Mammogram with Contextual Information Based on GANs
Shen, T., Hao, K., Gou, C., & Wang, F. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106019 · Cited by 76
Fast QR Code Detection in Arbitrarily Acquired Images
Belussi, L., & Hirata, N. S. T. (2011) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2011.16 · Cited by 76
A Faster RCNN-Based Pedestrian Detection System
Zhao, X., Li, W., Zhang, Y., Gulliver, T. A., Chang, S., & Feng, Z. (2016) · IEEE Vehicular Technology Conference · doi:10.1109/vtcfall.2016.7880852 · Cited by 76
2D–3D-based on-board pedestrian detection system
Gerónimo, D., Sappa, A. D., Ponsa, D., & López, A. M. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2009.07.008 · Cited by 75
Object Detection: Current and Future Directions
Verschae, R., & Ruíz-del-Solar, J. (2015) · Frontiers in Robotics and AI · doi:10.3389/frobt.2015.00029 · Cited by 75
Co-occurrence Histograms of Oriented Gradients for Human Detection
Watanabe, T., Ito, S., & Yokoi, K. (2010) · IPSJ Transactions on Computer Vision and Applications · doi:10.2197/ipsjtcva.2.39 · Cited by 74
Quantized wavelet features and support vector machines for on-road vehicle detection
Sun, Z., Bebis, G. N., & Miller, R. H. (2004) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2002.1235021 · Cited by 73
Rodin, C. D., de Lima, L. N., Andrade, F. A. A., Haddad, D. B., Johansen, T. A., & Storvold, R. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489465 · Cited by 73
Tavakoli, H. R., Borji, A., Laaksonen, J. T., & Rahtu, E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.03.018 · Cited by 72
Video analysis-based vehicle detection and tracking using an MCMC sampling framework
Arróspide, J., Salgado, L., & Nieto, M. (2012) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/1687-6180-2012-2 · Cited by 72
Pedestrian Detection for UAVs Using Cascade Classifiers with Meanshift
Aguilar, W. G., Luna, M. A., Moya, J. F., Abad, V., Parra, H., & Ruiz, H. (2017) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc.2017.83 · Cited by 72
Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection
Gerónimo, D., Sappa, A. D., López, A. M., & Ponsa, D. (2019) · PUB – Publications at Bielefeld University (Bielefeld University) · doi:10.2390/biecoll-icvs2007-81 · Cited by 71
Real-time pedestrian detection with deformable part models
Cho, H., Rybski, P. E., Bar-Hillel, A., & Zhang, W. (2012) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2012.6232264 · Cited by 70
Multi-Objective Optimization of Support Vector Machines
Suttorp, T., & Igel, C. (2006) · Springer eBooks · doi:10.1007/3-540-33019-4_9 · Cited by 70
Haar Wavelets and Edge Orientation Histograms for On–Board Pedestrian Detection
Gerónimo, D., López, A. M., Ponsa, D., & Sappa, A. D. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72847-4_54 · Cited by 70
Reliable Detection of Overtaking Vehicles Using Robust Information Fusion
Zhu, Y., Comaniciu, D., Pellkofer, M., & Koehler, T. (2006) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2006.883936 · Cited by 70
Real-time Detection of Vehicles Using the Haar-like Features and Artificial Neuron Networks
Atibi, M., Atouf, I., Mohamed, B., & Bennis, A. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.12.044 · Cited by 70
Daniilidis, K., Nikos Paragios, A., & Maragos, P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3 · Cited by 69
Evaluation and Acceleration of High-Throughput Fixed-Point Object Detection on FPGAs
Ma, X., Najjar, W. A., & Roy–Chowdhury, A. K. (2014) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2360030 · Cited by 69
A field model for human detection and tracking
Wu, Y., & Yu, T. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.87 · Cited by 69
Learning models for object recognition
Felzenszwalb, P. F. (2005) · 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.2001.990647 · Cited by 69
Pedestrian detection with super-resolution reconstruction for low-quality image
Jin, Y., Zhang, Y., Cen, Y., Li, Y., Mladenovic, V. M., & Voronin, V. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107846 · Cited by 68
FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection
Gochoo, M., Otgonbold, M.-E., Ganbold, E., Hsieh, J.-W., Chang, M., Chen, P.-Y., Dorj, B., Jassmi, H. A., Batnasan, G., Alnajjar, F., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00559 · Cited by 68
Automatic car detection in high resolution urban scenes based on an adaptive 3D-model
Hinz, S., Schlosser, C., & Reitberger, J. (2003) · IEEE GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (URBAN) · doi:10.1109/dfua.2003.1219980 · Cited by 68
A distributed visual surveillance system
Yuan, X., Sun, Z., Varol, Y., & Bebis, G. N. (2004) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2003.1217922 · Cited by 68
Hoang, V.-D., Le, M.-H., & Jo, K.-H. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.12.017 · Cited by 67
ALBERT-LUDWIGS-UNIVERSIT ¨ AT FREIBURG
Systeme, A. A. I., Burgard, W., & Domínguez, R. S. (2006) · Cited by 67
Human Detection by Quadratic Classification on Subspace of Extended Histogram of Gradients
Satpathy, A., Jiang, X., & Eng, H. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2264677 · Cited by 66
Two-Stage License Plate Detection Using Gentle Adaboost and SIFT-SVM
Ho, W. T., Lim, H. W., & Tay, Y. H. (2009) · Asian Conference on Intelligent Information and Database Systems · doi:10.1109/aciids.2009.25 · Cited by 66
Fast car detection using image strip features
Zheng, W., & Liang, L. (2009) · 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.2009.5206642 · Cited by 65
Human Object Detection in Forest with Deep Learning based on Drone’s Vision
Yong, S.-P., & Yeong, Y.-C. (2018) · International Conference on Computer and Information Sciences · doi:10.1109/iccoins.2018.8510564 · Cited by 65
Robust and fast pedestrian detection method for far-infrared automotive driving assistance systems
Liu, Q., Zhuang, J., & Ma, J. (2013) · Infrared Physics & Technology · doi:10.1016/j.infrared.2013.06.003 · Cited by 65
Airborne monitoring of vehicle activity in urban areas
Stilla, U., Michaelsen, E., Soergel, U., Hinz, S., & Ender, J. (2022) · Fraunhofer-Publica (Fraunhofer-Gesellschaft) · doi:10.24406/publica-fhg-347043 · Cited by 65
Detection and counting of cars in aerial images
Hinz, S. (2004) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2003.1247415 · Cited by 65
Efficient face detection by a cascaded support–vector machine expansion
Romdhani, S., Torr, P. H. S., Schölkopf, B., & Blake, A. (2004) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2004.1333 · Cited by 64
Human-centric design personalization of 3D glasses frame in markerless augmented reality
Huang, S.-H., Yang, Y.-I., & Chu, C. (2011) · Advanced Engineering Informatics · doi:10.1016/j.aei.2011.07.008 · Cited by 63
Improving the performance of on-road vehicle detection by combining Gabor and wavelet features
Sun, Z., Bebis, G. N., & Miller, R. H. (2003) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2002.1041201 · Cited by 63
On-line Boosting for Car Detection from Aerial Images
Thủy, N. T. T., Gräbner, H., Bischof, H., & Gruber, B. (2007) · RIVF International Conference on Computing and Communication Technologies (RIVF) · doi:10.1109/rivf.2007.369140 · Cited by 62
Accurate shade image matching by using a smartphone camera
Tam, W.-K., & Lee, H.-J. (2016) · Journal of Prosthodontic Research · doi:10.1016/j.jpor.2016.07.004 · Cited by 62
Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning
Walambe, R. A., Marathe, A., & Kotecha, K. V. (2021) · Drones · doi:10.3390/drones5030066 · Cited by 62
Stationary Features and Cat Detection
Fleuret, F., & Geman, D. (2007) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 62
YOLOD: A Target Detection Method for UAV Aerial Imagery
Luo, X., Wu, Y., & Zhao, L. (2022) · Remote Sensing · doi:10.3390/rs14143240 · Cited by 61
Preceding vehicle detection using Histograms of Oriented Gradients
Mao, L. T., Xie, M., Huang, Y., & Zhang, Y. (2010) · International Conference on Communications, Circuits and Systems · doi:10.1109/icccas.2010.5581983 · Cited by 61
Pedestrian detection and tracking using temporal differencing and HOG features
Barbu, T. (2014) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.12.004 · Cited by 61
From Tiger to Panda: Animal Head Detection
Zhang, W., Sun, J. Q., & Tang, X. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2099126 · Cited by 61
On Exploration of Classifier Ensemble Synergism in Pedestrian Detection
de Oliveira, L. R., Nunes, U., & Peixoto, P. (2009) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2009.2026447 · Cited by 61
Stereovision-based target tracking system for USV operations
Sinisterra, A. J., Dhanak, M., & von Ellenrieder, K. D. (2017) · Ocean Engineering · doi:10.1016/j.oceaneng.2017.01.024 · Cited by 61
Metadata
- Authors
- Papageorgiou, C., & Poggio, T.
- Affiliations
- Massachusetts Institute of Technology (US)
- ORCID
- Poggio, Tomaso
- Venue
- International Journal of Computer Vision
- Published
- 2000-06
- Volume
- 38
- Issue
- 1
- Pages
- 15-33
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W1608462934
- Cited by
- 1,289
- References
- 36
- Citations by year
- 2026: 6 · 2025: 24 · 2024: 24 · 2023: 36 · 2022: 39 · 2021: 56 · 2020: 27 · 2019: 37 · 2018: 47 · 2017: 55 · 2016: 57 · 2015: 78 · 2014: 98 · 2013: 97 · 2012: 88
- FWCI
- 22.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Funders
- National Science Foundation
- Massachusetts Institute of Technology
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- object detection, Haar wavelet transform, overcomplete dictionary, support vector machine, learning from examples, face detection, human detection, car detection, driver assistance systems, real-time detection, cluttered scenes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review