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 201–300 of 1,000
Automatic Vehicle Trajectory Extraction by Aerial Remote Sensing
Azevedo, C. L., Cardoso, J. L., Ben-Akiva, M. E., Costeira, J. P., & Marques, M. (2014) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2014.01.119 · Cited by 59
Occlusion Handling and Multi-Scale Pedestrian Detection Based on Deep Learning: A Review
Li, F., Li, X., Liu, Q., & Li, Z. (2022) · IEEE Access · doi:10.1109/access.2022.3150988 · Cited by 58
Driver head pose estimation using efficient descriptor fusion
Alioua, N., Amine, A., Rogozan, A., Bensrhair, A., & Rziza, M. (2016) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-016-0103-z · Cited by 58
An Evaluation of Local Shape-Based Features for Pedestrian Detection
Seemann, E., Leibe, B., Mikolajczyk, K., & Schiele, B. (2005) · British Machine Vision Conference (BMVC) · doi:10.5244/c.19.5 · Cited by 58
Transfer learning for pedestrian detection
Cao, X., Wang, Z., Yan, P., & Li, X. (2013) · Neurocomputing · doi:10.1016/j.neucom.2011.12.043 · Cited by 57
Pedestrian detection in automotive safety: Understanding state-of-the-art
Ragesh, N. K., & Rajesh, R. (2019) · IEEE Access · doi:10.1109/access.2019.2909992 · Cited by 57
Pedestrian Detection Based on HOG-LBP Feature
Gan, G., & Cheng, J. (2011) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis.2011.262 · Cited by 57
Grocery product detection and recognition
Franco, A., Maltoni, D., & Papi, S. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.02.050 · Cited by 57
BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation
Dhingra, N., Ritter, F., & Kunz, A. M. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00244 · Cited by 55
A novel shape-based non-redundant local binary pattern descriptor for object detection
Nguyen, D. T., Ogunbona, P., & Li, W. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.10.024 · Cited by 54
Single-Pedestrian Detection Aided by Two-Pedestrian Detection
Ouyang, W., Zeng, X., & Wang, X. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2377734 · Cited by 54
Halfawy, M. R., & Hengmeechai, J. (2014) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000312 · Cited by 54
Sparse auto-encoder based feature learning for human body detection in depth image
Su, S., Liu, Z., Xu, S., Li, S., & Ji, R. (2014) · Signal Processing · doi:10.1016/j.sigpro.2014.11.003 · Cited by 53
SUPPORT VECTOR MACHINE CLASSIFICATION OF PHYSICAL AND BIOLOGICAL DATASETS
Cai, C., Wang, W., & Chen, Y. (2003) · International Journal of Modern Physics C · doi:10.1142/s0129183103004759 · Cited by 53
Detection of unmanned aerial vehicles using a visible camera system
Hu, S., Goldman, G. H., & Borel-Donohue, C. C. (2017) · Applied Optics · doi:10.1364/ao.56.00b214 · Cited by 53
Porikli, F., & Yılmaz, A. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-28598-1_1 · Cited by 53
Learning based symmetric features selection for vehicle detection
liu, T., Zheng, N., Zhao, L., & Cheng, H.-Y. (2005) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2005.1505089 · Cited by 53
A Comprehensive Evaluation Framework and a Comparative Study for Human Detectors
Hussein, M. E., Porikli, F., & Davis, L. S. (2009) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2009.2026670 · Cited by 52
Fast human detection from videos using covariance features
Yao, J., & Odobez, J. (2008) · Cited by 52
Seamless Lesion Insertion for Data Augmentation in CAD Training
Pezeshk, A. X., Petrick, N., Chen, W.-J., & Sahiner, B. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2640180 · Cited by 52
Gawande, U. H., Hajari, K. O., & Golhar, Y. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.90810 · Cited by 52
Review of pedestrian detection techniques in automotive far‐infrared video
Hurney, P., Waldron, P., Morgan, F., Jones, E., & Glavin, M. (2015) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2014.0236 · Cited by 51
Liang, C.-W., & Juang, C. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.09.051 · Cited by 51
Li, K., He, F., Yu, H., & Chen, X. (2017) · Applied mathematics/Applied Mathematics. A Journal of Chinese Universities/Gao-xiao yingyong shuxue xuebao · doi:10.1007/s11766-017-3466-8 · Cited by 50
Image representations and feature selection for multimedia database search
Evgeniou, T., Pontil, M., Papageorgiou, C., & Poggio, T. (2003) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2003.1209008 · Cited by 50
Self-citationLearning to Recognize Three-Dimensional Objects
Roth, D., Yang, M., & Ahuja, N. (2002) · Neural Computation · doi:10.1162/089976602753633394 · Cited by 50
Weakly Supervised Learning of Visual Models and Its Application to Content-Based Retrieval
Schmid, C. (2003) · International Journal of Computer Vision · doi:10.1023/b:visi.0000004829.38247.b0 · Cited by 50
Felzenszwalb, P. F. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130311 · Cited by 50
Car detection in aerial thermal images by local and global evidence accumulation
Hinz, S., & Stilla, U. (2005) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.08.013 · Cited by 49
Deep learning for occluded and multi‐scale pedestrian detection: A review
Xiao, Y., Zhou, K., Cui, G., Jia, L., Fang, Z., Yang, X., & Xia, Q. (2020) · IET Image Processing · doi:10.1049/ipr2.12042 · Cited by 49
Deep learning predicts microbial interactions from self-organized spatiotemporal patterns
Lee, J., Sadler, N., Egbert, R. G., Anderton, C., Hofmockel, K., Jansson, J. K., & Song, H. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.05.023 · Cited by 48
Object of Interest Detection by Saliency Learning
Khuwuthyakorn, P., Robles‐Kelly, A., & Zhou, J. W. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15552-9_46 · Cited by 48
Automatic detection and tracking of pedestrians from a moving stereo rig
Konrad Schindler, B., Ess, A., Leibe, B., & Gool, L. V. (2010) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.06.006 · Cited by 48
Efficient Stixel-based object recognition
Enzweiler, M., Hummel, M., Pfeiffer, D., & Franke, U. (2012) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2012.6232137 · Cited by 47
Monocular pedestrian recognition using motion parallax
Enzweiler, M., Kanter, P., & Gavrila, D. M. (2008) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2008.4621169 · Cited by 47
Configuration Estimates Improve Pedestrian Finding
Tran, D., & Forsyth, D. A. (2007) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 47
A Real-Time Deformable Detector
Ali, K., Fleuret, F., Hasler, D., & Fua, P. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.117 · Cited by 46
Efficient object detection using cascades of nearest convex model classifiers
Çevıkalp, H., & Triggs, B. (2012) · 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.2012.6248047 · Cited by 46
A survey on human detection surveillance systems for Raspberry Pi
Khalifa, A. F., Badr, E., & Elmahdy, H. N. (2019) · Image and Vision Computing · doi:10.1016/j.imavis.2019.02.010 · Cited by 46
Classification by evolutionary ensembles
Wang, X., & Wang, H. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2005.09.016 · Cited by 46
Shared Features for Scalable Appearance-Based Object Recognition
Murphy-Chutorian, E., & Triesch, J. (2005) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/acvmot.2005.109 · Cited by 46
Pyramid binary pattern features for real-time pedestrian detection from infrared videos
Sun, H., Wang, C., Wang, B., & El‐Sheimy, N. (2010) · Neurocomputing · doi:10.1016/j.neucom.2010.10.009 · Cited by 45
Stereo regions-of-interest selection for pedestrian protection: A survey
Llorca, D. F., Sotelo, M. Á., Hellín, A. M., Orellana, A. F. B., Gavilán, M., Daza, I. G., & Lorente, A. G. (2012) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2012.06.006 · Cited by 45
Semi-Local Structure Patterns for Robust Face Detection
Jeong, K., Choi, J., & Jang, G. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2372762 · Cited by 45
A Large Dimensional Analysis of Least Squares Support Vector Machines
Liao, Z., & Couillet, R. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2889954 · Cited by 45
ECG Image Classification in Real time based on the Haar-like Features and Artificial Neural Networks
Mohamed, B., Atouf, I., Atibi, M., & Bennis, A. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.12.045 · Cited by 45
Performance evaluation of local features in human classification and detection
Paisitkriangkrai, S., Shen, C., & Zhang, J. (2008) · IET Computer Vision · doi:10.1049/iet-cvi:20080026 · Cited by 44
Fast Component-Based QR Code Detection in Arbitrarily Acquired Images
Belussi, L., & Hirata, N. S. T. (2012) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-012-0355-x · Cited by 44
Adaptive Sliding-Window Strategy for Vehicle Detection in Highway Environments
Noh, S., Shim, D., & Jeon, M. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2466652 · Cited by 44
Barla, A., Franceschi, E., Odone, F., & Verri, A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45665-1_7 · Cited by 44
Robust and fast detection of moving vehicles in aerial videos using sliding windows
Teutsch, M., & Krüger, W. A. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301396 · Cited by 43
3D Haar-Like Features for Pedestrian Detection
Cui, X., Liu, Y., Shan, S., Chen, X., & Gao, W. (2007) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2007.4284887 · Cited by 43
Vision-based bicycle detection and tracking using a deformable part model and an EKF algorithm
Cho, H., Rybski, P. E., & Zhang, W. (2010) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2010.5624993 · Cited by 42
Robust people counting in video surveillance: Dataset and system
Li, J., Huang, L., & Liu, C. (2011) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2011.6027294 · Cited by 42
Robust detection of non-motorized road users using deep learning on optical and LIDAR data
Kim, T., & Ghosh, J. (2016) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795566 · Cited by 42
Efficient vehicle tracking and classification for an automated traffic surveillance system
Ambardekar, A. (2007) · Cited by 42
Multiclass Multimodal Detection and Tracking in Urban Environments
Spinello, L., Triebel, R., & Siegwart, R. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910377533 · Cited by 42
Ensemble of Multiple Pedestrian Representations
Nanni, L., & Lumini, A. (2008) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2008.922882 · Cited by 42
Advances in Component-Based Face Detection
Bileschi, S., & Heisele, B. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45665-1_11 · Cited by 42
DMA-YOLO: multi-scale object detection method with attention mechanism for aerial images
Li, Y., Feng, Y., Zhou, M., Xiong, X., Wang, Y., & Qiang, B. (2023) · The Visual Computer · doi:10.1007/s00371-023-03095-3 · Cited by 41
Awareness of Road Scene Participants for Autonomous Driving
Petrovskaya, A., Perrollaz, M., de Oliveira, L. R., Spinello, L., Triebel, R., Makris, A., Yoder, J.-D., Laugier, C., Nunes, U., & Bessìère, P. (2012) · Springer eBooks · doi:10.1007/978-0-85729-085-4_54 · Cited by 41
Learning from adversarial medical images for X-ray breast mass segmentation
Shen, T., Gou, C., Wang, F., He, Z., & Chen, W. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105012 · Cited by 41
Real-Time Detection of River Surface Floating Object Based on Improved RefineDet
Zhang, L., Wei, Y., Wang, H., Shao, Y., & Shen, J. (2021) · IEEE Access · doi:10.1109/access.2021.3085348 · Cited by 41
Joint Attention in Driver-Pedestrian Interaction: from Theory to PracticePreprint
Rasouli, A., & Tsotsos, J. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.02522 · Cited by 41
A robust algorithm for detecting people in overhead views
Ahmed, I., & Adnan, A. (2017) · Cluster Computing · doi:10.1007/s10586-017-0968-3 · Cited by 40
Face and landmark detection by using cascade of classifiers
Çevıkalp, H., Triggs, B., & Franc, V. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553705 · Cited by 39
RGB-D, Laser and Thermal Sensor Fusion for People following in a Mobile Robot
Susperregi, L., Martínez‐Otzeta, J. M., Ansuategi, A., Ibarguren, A., & Sierra, B. (2013) · International Journal of Advanced Robotic Systems · doi:10.5772/56123 · Cited by 39
Scale Space Histogram of Oriented Gradients for Human Detection
He, N., Cao, J., & Song, L. (2008) · International Symposium on Information Science and Engineering · doi:10.1109/isise.2008.308 · Cited by 39
Disparity Statistics for Pedestrian Detection: Combining Appearance, Motion and Stereo
Walk, S., Konrad Schindler, B., & Schiele, B. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3_14 · Cited by 39
Efficient Pedestrian Detection via Rectangular Features Based on a Statistical Shape Model
Zhang, S., Bauckhage, C., & Cremers, A. B. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2341042 · Cited by 38
City-Wide Traffic Flow Estimation From a Limited Number of Low-Quality Cameras
Idé, T., Katsuki, T., Morimura, T., & Morris, R. A. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2597160 · Cited by 38
Occlusion and multi-scale pedestrian detection A review
Chen, W., Zhu, Y., Tian, Z., Zhang, F., & Yao, M. (2023) · Array · doi:10.1016/j.array.2023.100318 · Cited by 38
Visual People Detection - Different Models, Comparison and Discussion
Schiele, B., Andriluka, M., Majer, N., Roth, S., & Wojek, C. (2009) · Cited by 38
AdaBoost Learning for Human Detection Based on Histograms of Oriented Gradients
Wang, C.-C. R., & Lien, J.-J. J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-76386-4_84 · Cited by 38
Applying multi layer homography for multi camera person tracking
Arsić, D., Hristov, E., Lehment, N. H., Hörnler, B., Schuller, B. W., & Rigoll, G. (2008) · ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) · doi:10.1109/icdsc.2008.4635731 · Cited by 38
Vehicle recognition and TTC estimation at night based on spotlight pairing
Gonner, S., Müller, D., Hold, S., Meuter, M., & Kummert, A. (2009) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2009.5309848 · Cited by 38
Learning semantic object parts for object categorization
Leibe, B., Ettlin, A., & Schiele, B. (2007) · Image and Vision Computing · doi:10.1016/j.imavis.2007.08.012 · Cited by 38
A comprehensive survey on image dehazing for different atmospheric scattering models
An, S., Huang, X., Cao, L., & Wang, L. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17292-8 · Cited by 37
Vision-based pedestrian detection for rear-view cameras
Silberstein, S., Levi, D., Kogan, V., & Gazit, R. (2014) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2014.6856399 · Cited by 37
Class-specific object proposals re-ranking for object detection in automatic driving
Zhong, Z., Lei, M., Cao, D., Jianping Fan, B., & Li, S. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.02.068 · Cited by 37
A Sparsity-Enforcing Method for Learning Face Features
Destrero, A., Mol, C. D., Odone, F., & Verri, A. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.2007610 · Cited by 37
DETRAC: A New Benchmark and Protocol for Multi-Object TrackingPreprint
Wen, L., Du, D., Cai, Z., Lei, Z., Chang, M., Qi, H., Lim, J., Yang, M., & Lyu, S. (2015) · arXiv (Cornell University) · Cited by 37
Real-time Pedestrian and Vehicle Detection for Autonomous Driving
Yang, Z., Li, J., & Li, H. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500642 · Cited by 36
Person detector for different overhead views using machine learning
Ahmed, I., Ahmad, M., Adnan, A., Ahmad, A., & Khan, M. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-019-00950-5 · Cited by 36
Efficient topview person detector using point based transformation and lookup table
Ahmed, I., Ahmad, M., Nawaz, M., Haseeb, K., Khan, S., & Jeon, G. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.08.015 · Cited by 36
Youssef, Y., & Elshenawy, M. (2021) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198121997833 · Cited by 36
A framework for visual-context-aware object detection in still images
Perko, R., & Leonardis, A. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.03.005 · Cited by 36
Vision-based bicyclist detection and tracking for intelligent vehicles
Cho, H., Rybski, P. E., & Zhang, W. (2010) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2010.5548063 · Cited by 36
Radar-Vision Based Vehicle Recognition with Evolutionary Optimized and Boosted Features
Kadow, U., Schneider, G., & Vukotich, A. (2007) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2007.4290206 · Cited by 36
Zhan, M., Crane, M. M., Entchev, E. V., Caballero, A., de Abreu, D. A. F., Ch'ng, Q.-L., & Lu, H. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004194 · Cited by 35
People Detection Using Color and Depth Images
Salas, J., & Tomasi, C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21587-2_14 · Cited by 35
Target-directed attention: Sequential decision-making for gaze planning
Vogel, J., & de Freitas, N. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543568 · Cited by 35
Fast human detection with cascaded ensembles on the GPU
Bilgiç, B., Horn, B. K. P., & Masaki, I. (2010) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2010.5548145 · Cited by 35
Virtual-reality-based digital twin of office spaces with social distance measurement feature
Mukhopadhyay, A., Reddy, G. S. R., Saluja, K. S., Ghosh, S., Peña-Ríos, A., Gopal, G., & Biswas, P. (2022) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.01.004 · Cited by 34
Zhang, G., Gao, F., Liu, C., Liu, W., & Yuan, H. (2010) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2010.5582537 · Cited by 34
Watching Unlabeled Video Helps Learn New Human Actions from Very Few Labeled Snapshots
Chen, C.-Y., & Grauman, K. L. (2013) · 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.2013.80 · Cited by 33
Pedestrian Detection for UAVs Using Cascade Classifiers and Saliency Maps
Aguilar, W. G., Luna, M. A., Moya, J. F., Abad, V., Ruiz, H., Parra, H., & Ángulo, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59147-6_48 · Cited by 33
Chen, Z., Chen, K., & Chen, J. (2013) · International Conference on Computer Sciences and Applications · doi:10.1109/csa.2013.92 · Cited by 33
A trainable system for grading fish from images
Odone, F., Trucco, E., & Verri, A. (2001) · Applied Artificial Intelligence · doi:10.1080/088395101317018573 · Cited by 33
An Improved Pedestrian Detection Algorithm Integrating Haar-Like Features and HOG Descriptors
Wei, Y., Tian, Q., & Guo, T. (2013) · Advances in Mechanical Engineering · doi:10.1155/2013/546206 · Cited by 33
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