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A Trainable System for Object Detection
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“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]
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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
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Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function
Zhang, Y., Xu, L., & Zhang, Y. (2022) · Measurement and Control · doi:10.1177/00202940221110164 · Cited by 12
PFEL-Net: A lightweight network to enhance feature for multi-scale pedestrian detection
Tang, J., Lai, H., Gao, G., & Wang, T. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.102198 · Cited by 12
Generic Object Detection Using Improved Gentleboost Classifier
Guo, L., Liao, Y., Luo, D., & Liao, H. (2012) · Physics Procedia · doi:10.1016/j.phpro.2012.03.272 · Cited by 12
A survey of Haar-Like feature representation
Oualla, M., Sadiq, A., & Mbarki, S. (2014) · International Conference on Multimedia Computing and Systems · doi:10.1109/icmcs.2014.6911186 · Cited by 12
Guan, C.-N., Juang, C., & Chen, G.-C. (2012) · Digital Signal Processing · doi:10.1016/j.dsp.2012.05.011 · Cited by 12
A Novel Method for Day Time Pedestrian Detection
Behera, R. K., Gangadharan, J., Kutty, K. K., Nair, S., & Vaidya, V. G. (2015) · SAE International journal of passenger cars. Electronic and electrical systems · doi:10.4271/2015-01-0319 · Cited by 12
Pedestrian Detection Using Augmented Training Data
Nilsson, J., Andersson, P., Gu, I. Y., & Fredriksson, J. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.778 · Cited by 12
Accuracy Performance of a Time-of-Flight CMOS Range Image Sensor System
Leonardi, F., Covi, D., Petri, D., & Stoppa, D. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2009.2014514 · Cited by 12
Joint components based pedestrian detection in crowded scenes using extended feature descriptors
Hoang, V.-D., & Jo, K.-H. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.11.104 · Cited by 12
Improving Pedestrian Detection Using Support Vector Regression
Errami, M., & Rziza, M. (2016) · International Conference on Computer Graphics, Imaging and Visualization (CGIV) · doi:10.1109/cgiv.2016.38 · Cited by 12
NAVI: Navigation aid for the visually impaired
Sharma, T., Apoorva, J., Lakshmanan, R., Gogia, P. C., & Kondapaka, M. (2016) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2016.7813856 · Cited by 12
A Real-Time Active Pedestrian Tracking System Inspired by the Human Visual System
Wang, Y., Zhao, Q., Wang, B., Wang, S., Zhang, Y., Guo, W., & Feng, Z. (2015) · Cognitive Computation · doi:10.1007/s12559-015-9334-z · Cited by 12
Parking Lot Space Detection Based On Image Processing
Ashqer, M. I., & Bikdash, M. U. (2019) · IEEE SoutheastCon · doi:10.1109/southeastcon42311.2019.9020584 · Cited by 12
Learning a Family of Detectors via Multiplicative Kernels
Yuan, Q., Thangali, A., Ablavsky, V., & Sclaroff, S. (2013) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-94-007-0726-9_1 · Cited by 12
People Detection in Color and Infrared Video Using HOG and Linear SVM
Tribaldos, P., Serrano-Cuerda, J., López, M. T., Fernández‐Caballero, A., & López-Sastre, R. J. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38622-0_19 · Cited by 12
People detection in low-resolution video with non-stationary background
Zhang, J., & Gong, S. (2008) · Image and Vision Computing · doi:10.1016/j.imavis.2008.06.013 · Cited by 12
Human Sensing Using Computer Vision for Personalized Smart Spaces
Surie, D., Partonia, S., & Lindgren, H. (2013) · Autonomic and Trusted Computing · doi:10.1109/uic-atc.2013.24 · Cited by 12
A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition
Yang, M., Roth, D., & Ahuja, N. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47979-1_46 · Cited by 12
Spatial-Temporal Granularity-Tunable Gradients Partition (STGGP) Descriptors for Human Detection
Liu, Y., Shan, S., Chen, X., Heikkilä, J., Gao, W., & Pietikäinen, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15549-9_24 · Cited by 12
A theory of the quasi-static world
Sanders, B., Nelson, R. C., & Sukthankar, R. (2003) · doi:10.1109/icpr.2002.1047781 · Cited by 12
EYOLOv3: An Efficient Real-Time Detection Model for Floating Object on River
Zhang, L., Xie, Z., Xu, M., Zhang, Y., & Wang, G. (2023) · Applied Sciences · doi:10.3390/app13042303 · Cited by 11
A Comprehensive Review on Group Re-identification in Surveillance Videos
Nayak, K., & Dogra, D. P. (2025) · ACM Computing Surveys · doi:10.1145/3711126 · Cited by 11
A learning-based contrarian trading strategy via a dual-classifier model
Huang, S.-H., Lai, S., & Tai, S.-H. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961192 · Cited by 11
Car detection in sequences of images of urban environments using mixture of deformable part models
Leon, L. M. C., & Hirata, R. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.10.028 · Cited by 11
Multi-sparse descriptor: A scale invariant feature for pedestrian detection
Liu, Y., Lasang, P., Siegel, M., & Sun, Q. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.07.143 · Cited by 11
Zhou, Y. Z., Xia, L., Zhao, J., Yao, R., & Liu, B. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15608-2 · Cited by 11
Real-Time Estimation of Optical Flow Based on Optimized Haar Wavelet Features
Salmen, J., Caup, L., & Igel, C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19893-9_31 · Cited by 11
A Variational Statistical Framework for Object Detection
Fan, W., Bouguila, N., & Ziou, D. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24958-7_32 · Cited by 11
Hoang, V.-D., Le, M.-H., & Jo, K.-H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34630-9_7 · Cited by 11
Pedestrian detection in low resolution videos
Sager, H., & Hoff, W. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836038 · Cited by 11
Automatic categorization-based multi-stage pedestrian detection
Yang, K., Du, E. Y., Jiang, P., Chen, Y., Sherony, R., & Takahashi, H. (2012) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2012.6338874 · Cited by 11
Hohm, A., Wojek, C., Schiele, B., & Winner, H. (2008) · Max Planck Digital Library · Cited by 11
Weighted Unsupervised Learning for 3D Object Detection
Kowsari, K., & Manal, H. (2016) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2016.070180 · Cited by 11
Learning rich features from objectness estimation for human lying-pose detection
Xia, D., Su, S., Geng, L.-C., Wu, G., & Li, S. (2016) · Multimedia Systems · doi:10.1007/s00530-016-0518-5 · Cited by 11
Pedestrian detection by learning a mixture mask model and its implementation
Chen, Y., Zhang, L., Liu, X., & Chen, C. (2016) · Information Sciences · doi:10.1016/j.ins.2016.08.050 · Cited by 11
Chen, H.-K., Zhao, X., Sun, S., & Tan, M. H. (2017) · Machine Intelligence Research · doi:10.1007/s11633-016-1029-8 · Cited by 11
Tang, Y., & Verma, N. (2018) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2018.2808104 · Cited by 11
Metric tensor and Christoffel symbols based 3D object categorization
Ganihar, S. A., Joshi, S., Shetty, S., & Mudenagudi, U. (2014) · ACM SIGGRAPH Posters · doi:10.1145/2614217.2630582 · Cited by 11
How to improve the HOG detector in the UAV context
Blondel, P., Potelle, A., Pégard, P. C., & Lozano, P. R. (2013) · IFAC Proceedings Volumes · doi:10.3182/20131120-3-fr-4045.00009 · Cited by 11
Image-processing algorithms for detecting and counting vehicles waiting at a traffic light
de la Rocha, E., & Palacios, R. (2010) · Journal of Electronic Imaging · doi:10.1117/1.3528465 · Cited by 11
An enhanced Histogram of Oriented Gradient for pedestrian detection
Wei, D., Zhao, Y. J., Cheng, R., & Li, G. (2013) · International Conference on Intelligent Control and Information Processing · doi:10.1109/icicip.2013.6568118 · Cited by 11
Experiments with rough set approach to face recognition
Chen, X., & Ziarko, W. (2011) · International Journal of Intelligent Systems · doi:10.1002/int.20481 · Cited by 11
An experimental study on pedestrian classification using local features
Paisitkriangkrai, S., Shen, C., & Zhang, J. (2008) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2008.4542024 · Cited by 11
Feature selection by AdaBoost for SVM-based face detection
Le, D.-D. (2004) · Cited by 11
Goyani, M. M., & Patel, N. M. (2017) · International Journal of Computers and Applications · doi:10.1080/1206212x.2017.1395134 · Cited by 11
Enhancing Real-Time Human Detection Based on Histograms of Oriented Gradients
Pedersoli, M., Gonzàlez, J., Chakraborty, B., & Villanueva, J. J. (2007) · Advances in intelligent and soft computing · doi:10.1007/978-3-540-75175-5_91 · Cited by 11
Improved Wavelet Feature Extraction Methods Based on HSV Space for Vehicle Detection
Wen, X., Liu, W., Wang, N., Yuan, H., & Zhao, H. (2007) · Cited by 11
Contour-motion feature (CMF): A space–time approach for robust pedestrian detection
Liu, Y., Chen, X., Yao, H., Cui, X., Liu, C., & Gao, W. Z. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.03.007 · Cited by 11
Pham, T. V., & Smeulders, A. W. M. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.05.008 · Cited by 11
Bergboer, N. H. (2007) · Research Publications (Maastricht University) · doi:10.26481/dis.20071010nb · Cited by 11
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Bose, B. (2004) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 11
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van der Burg, V., Akdag Salah, A., Chandrasegaran, S., & Lloyd, P. (2022) · Proceedings of DRS · doi:10.21606/drs.2022.653 · Cited by 10
Bicyclist detection in large scale naturalistic driving video
Yang, K., Liu, C., Zheng, J. Y., Christopher, L., & Chen, Y. (2014) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2014.6957928 · Cited by 10
Joint global–local information pedestrian detection algorithm for outdoor video surveillance
Hu, H., Zhang, X., Zhang, W., & Li, B. H. (2014) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2014.11.009 · Cited by 10
Person re-identification combining deep features and attribute detection
Watson, G., & Bhalerao, A. H. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08499-9 · Cited by 10
Pedestrian detection using quaternion histograms of oriented gradients
Lian, G. (2020) · IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) · doi:10.1109/icpics50287.2020.9202071 · Cited by 10
Wen, Y., Ke, W., & Sheng, H. (2024) · Applied Sciences · doi:10.3390/app14062494 · Cited by 10
Discrete features for rapid pedestrian detection in infrared images
Olmeda, D., Armingol, J. M., & de la Escalera, A. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6385928 · Cited by 10
A corpus for benchmarking of people detection algorithms
García‐Martín, Á., Martínez, J. M., & Bescós, J. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.09.038 · Cited by 10
Image processing and vision techniques for smart vehicles
Haq, E. U., Pirzada, S. J. H., Piao, J., Yu, T., & Shin, H. (2012) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2012.6271453 · Cited by 10
Model of top-down / bottom-up visual attention for location of salient objects in specific domains
Benicasa, A. X., Zhao, L., & Romero, R. A. F. (2012) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252585 · Cited by 10
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Walia, G. S., & Kapoor, R. (2014) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001414550040 · Cited by 10
Vaidehi, V., Fathima, A. A., Ramanathan, S., Sameer, N. M., & Sagar, S. P. (2011) · International Conference on Recent Trends in Information Technology · doi:10.1109/icrtit.2011.5972251 · Cited by 10
An algorithm based on SVM ensembles for motorcycle recognition
Wen, X., Yuan, H., Song, C., Liu, W., & Zhao, H. (2007) · IEEE International Conference on Vehicular Electronics and Safety (ICVES) · doi:10.1109/icves.2007.4456371 · Cited by 10
Duives, D. C., Daamen, W., & Hoogendoorn, S. P. (2018) · Journal of Advanced Transportation · doi:10.1155/2018/7328074 · Cited by 10
Yussiff, A.-L., Yong, S.-P., & Baharudin, B. (2013) · Lecture notes in electrical engineering · doi:10.1007/978-3-642-41674-3_159 · Cited by 10
Marine life observation using classification algorithms on ocean surface photographs
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A context-based model of attention
Bergboer, N. H., Postma, E. O., & van den Herik, H. J. (2004) · European Conference on Artificial Intelligence · Cited by 10
Khan, A., Khan, M. U. K., Bilal, M., & Kyung, C. (2015) · IFIP/IEEE International Conference on Very Large Scale Integration · doi:10.1109/vlsi-soc.2015.7314406 · Cited by 10
Srivastava, G. K., Yoder, J. A., Park, J., & Kak, A. C. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.06.009 · Cited by 10
Counting Vehicles in Highway Surveillance Videos
Tamersoy, B., & Aggarwal, J. K. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.886 · Cited by 10
Usage of HoG (histograms of oriented gradients) features for victim detection at disaster areas
Uzun, Y., Balcılar, M., Mahmoodi, K., Davletov, F., Amasyalı, M. F., & Yavuz, S. (2013) · National Conference on Electrical and Electronics Engineering (ELECO) · doi:10.1109/eleco.2013.6713903 · Cited by 10
Video object detection speedup using staggered sampling
Greig, D. (2009) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2009.5403129 · Cited by 10
Model-based detection and segmentation of vehicles for intelligent transportation system
Eng, H., Thida, M., Chew, B. F., Leman, K., & Anggrelly, S. Y. (2008) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2008.4582895 · Cited by 10
Parametric distortion-adaptive neighborhood for omnidirectional camera
Tang, Y., Li, Y., & Luo, J. (2015) · Applied Optics · doi:10.1364/ao.54.006969 · Cited by 10
Pedestrian Recognition Using High-definition LIDAR
Kidono, K., Watanabe, A., Naito, T., & Miura, J. (2011) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.29.963 · Cited by 10
Marine life airborne observation using HOG and SVM classifier
Alsahwa, B., Maussang, F., Garello, R., & Chevallier, A. (2016) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans.2016.7761242 · Cited by 10
An auxiliary gaze point estimation method based on facial normal
Sun, W., Sun, N., Guo, B., Jia, W., & Sun, M. (2014) · Pattern Analysis and Applications · doi:10.1007/s10044-014-0407-5 · Cited by 10
Pedestrian Detection Using Coarse-to-Fine Method with Haar-Like and Shapelet Features
Wang, Y., Xing, J., Luo, X., & Zhang, J. (2010) · International Conference on Multimedia Technology (ICMT) · doi:10.1109/icmult.2010.5630446 · Cited by 10
High performance sensor fusion architecture for vision-based occupant detection
Owechko, Y., Srinivasa, N., Medasani, S., & Boscolo, R. (2004) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2003.1252661 · Cited by 10
Vehicle detection and recognition for automated guided vehicle
Truong, Q. B., Geon, H. N., & Lee, B. R. (2009) · 2009 ICCAS-SICE · Cited by 10
A biological model of object recognition with feature learning
Louie, J. (2003) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 10
Chavéz-García, R. O. (2014) · theses.fr (ABES) · doi:10.70675/01256d75z2f44z40f4za3f8z25f7f06f3d51 · Cited by 10
Pratap, A., Kumar, S., & Chakravarty, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5112107 · Cited by 10
Vision-based indoor occupants detection system for intelligent buildings
Liu, D., Du, Y., Zhao, Q., & Guan, X. (2012) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist.2012.6295489 · Cited by 9
Human Detection Using Wavelet-Based CS-LBP and a Cascade of Random Forests
Kim, D.-Y., Kwak, J.-Y., Ko, B. C., & Nam, J.-Y. (2012) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2012.124 · Cited by 9
An evaluation on robustness and brittleness of HOG features of human detection
Ninomiya, H., Ohki, H., Gyohten, K., & Sueda, N. (2011) · Korea-Japan Joint Workshop on Frontiers of Computer Vision · doi:10.1109/fcv.2011.5739746 · Cited by 9
Satpathy, A., Jiang, X., & Eng, H. (2011) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2011.16 · Cited by 9
Fast pedestrian detection using a modified WLD detector in salient region
Lian, G., Lai, J., & Yang, Y. (2011) · International Conference on System Science and Engineering · doi:10.1109/icsse.2011.5961966 · Cited by 9
Frame-rate vehicle detection within the attentional visual area of drivers
Zabihi, S. M., Beauchemin, S. S., Medeiros, E. A. M. D., & Bauer, M. (2014) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2014.6856601 · Cited by 9
Cho, H.-M., & Hwang, S.-Y. (2015) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-015-0074-5 · Cited by 9
A Configurable Parallel Hardware Architecture for Efficient Integral Histogram Image Computing
Yin, S., Ouyang, P., Chen, T., Liu, L., & Wei, S. (2015) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2015.2462752 · Cited by 9
Enhanced LSB Steganography with people detection as stego key generator
Pande Gede Pradnya Jaya, S. T., Hidayat, B., & Suratman, F. Y. (2017) · IEEE International Conference on Signals and Systems (ICSigSys) · doi:10.1109/icsigsys.2017.7967078 · Cited by 9
Automatic Analysis of Pedestrian’s Body Language in the Interaction with Autonomous Vehicles
Morales-Alvarez, W., Gomez-Silva, M. J., Fernández, G., GarcA-Fernandez, F., & Olaverri-Monreal, C. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500425 · Cited by 9
Ding, X., Gan, Q., & Bahrami, S. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4574 · Cited by 9
Lee, K.-D., Wang, B., & Lee, S. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20031770 · Cited by 9
Boosting histograms of oriented gradients for human detection
Perdersoli, M., Gonzàlez, J., Chakraborty, B., & Villanueva, J. J. (2007) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · Cited by 9
Detection of partially occluded pedestrians by an enhanced cascade detector
Li, W., Ni, H., Wang, Y., Fu, B., Liu, P., & -, S. W. (2014) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2012.0173 · Cited by 9
Image Segmentation using Salient Points-Based Object Templates
Zhang, H., & Goldman, S. A. (2006) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2006.312424 · Cited by 9
Pedestrian Detection Based on Clustered Poselet Models and Hierarchical and–or Grammar
Li, B., Chen, Y., & Wang, F. (2014) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2014.2331314 · Cited by 9
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
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