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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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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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Items 701–800 of 1,000
Obstacles and Pedestrian Detection on a Moving Vehicle
Yang, Y., Ren, M., & Jingyu, Y. (2014) · International Journal of Advanced Robotic Systems · doi:10.5772/58315 · Cited by 6
Pro-active optimal control for semi-active vehicle suspension based on sensitivity updates
Michael, J., & Gerdts, M. (2015) · Vehicle System Dynamics · doi:10.1080/00423114.2015.1081953 · Cited by 6
Metric Tensor and Christoffel Symbols Based 3D Object Categorization
Ganihar, S. A., Joshi, S., Gangisetty, S., & Mudenagudi, U. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16634-6_11 · Cited by 6
Unsupervised object counting without object recognition
Katsuki, T., Morimura, T., & Idé, T. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900197 · Cited by 6
Lahmyed, R., Ansari, M. E., & Kerkaou, Z. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13026-4 · Cited by 6
From a Visual Scene to a Virtual Representation: A Cross-Domain Review
Pereira, A., Carvalho, P., Pereira, N., Viana, P., & Côrte-Real, L. (2023) · IEEE Access · doi:10.1109/access.2023.3283495 · Cited by 6
Efficient human detection in crowded environment
Hu, M., Cheng, W.-H., Hu, C.-S., Wu, J., & Li, J.-W. (2014) · Multimedia Systems · doi:10.1007/s00530-014-0391-z · Cited by 6
Pedestrian detection using histograms of Oriented Gradients of granule feature
Chan, Y.-M., Fu, L., Hsiao, P., & Lo, M.-F. (2013) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2013.6629664 · Cited by 6
Enhancement of human body detection and tracking algorithm based on Viola and Jones framework
Krerngkamjornkit, R., & Simić, M. (2013) · International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services · doi:10.1109/telsks.2013.6704903 · Cited by 6
Design and implementation of a high performance pedestrian detection
Prioletti, A., Grisleri, P., Trivedi, M. M., & Broggi, A. (2013) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2013.6629662 · Cited by 6
Robust Human Detection with Low Energy Consumption in Visual Sensor Network
Fu, H., Ma, H., & Liu, L. (2011) · International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) · doi:10.1109/msn.2011.84 · Cited by 6
Extraction of Characters on Signboards in Natural Scene Images by Stump Classifiers
Maruyama, M., & Yamaguchi, T. (2009) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2009.147 · Cited by 6
Boosted Vehicle Detection Using Local and Global Features
Lin, C., Hsu, S.-C., Lee, J.-F., & Yang, C.-T. (2013) · Journal of Signal and Information Processing · doi:10.4236/jsip.2013.43032 · Cited by 6
A novel approach to improve intelligent traffic management system (ITMS)
Garg, A. (2015) · International Conference on Green Computing and Internet of Things (ICGCIoT) · doi:10.1109/icgciot.2015.7380549 · Cited by 6
Designing a Boosted Classifier on Riemannian Manifolds
Porikli, F., Tuzel, O., & Meer, P. (2015) · Springer eBooks · doi:10.1007/978-3-319-22957-7_13 · Cited by 6
A Literature Review on Crowd Scene Analysis and Monitoring
Lamba, S., & Nain, N. (2016) · International Journal of Urban Design for Ubiquitous Computing · doi:10.21742/ijuduc.2016.4.2.02 · Cited by 6
Human Recognition Using RFID Technology and Stereo Vision
Jia, S., Sheng, J., Chugo, D., & Takase, K. (2009) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2009.p0028 · Cited by 6
Vehicle detection on a pint-sized computer
Tangkocharoen, T., & Srisuphab, A. (2017) · International Conference on Knowledge and Smart Technology · doi:10.1109/kst.2017.7886078 · Cited by 6
Research on pedestrian detection technology based on improved DPM model
Cai, Y., Liu, Z., Sun, X., Chen, L. F., & Wang, H. (2017) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber.2017.8446066 · Cited by 6
Generative detect for occlusion object based on occlusion generation and feature completing
Xu, C., Lang, W., Xin, R., Mao, K., & Jiang, H. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103189 · Cited by 6
MULTIPLE-PERSON TRACKING SYSTEM FOR CONTENT ANALYSIS
Hsieh, J.-W., & HUANG, Y.-S. (2002) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001402001800 · Cited by 6
SVM-Based Object Detection Using Self-quotient ε-Filter and Histograms of Oriented Gradients
Matsumoto, M. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-27534-0_18 · Cited by 6
Choi, C., & Christensen, H. I. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354590 · Cited by 6
Ahlberg, J., Arsić, D., Ganchev, T. D., Linderhed, A., Menezes, P. J., Ntalampiras, S., Olma, T., Potamitis, I., & Ros, J. (2008) · DiVA (Linkoping University) · Cited by 6
Shape Based People Detection for Visual Surveillance Systems
Leo, M., Spagnolo, P., Attolico, G., & Distante, A. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44887-x_34 · Cited by 6
Video saliency detection with robust temporal alignment and local-global spatial contrast
Ren, Z., Chia, L.-T., & Rajan, D. (2012) · International Conference on Multimedia Retrieval · doi:10.1145/2324796.2324851 · Cited by 6
Offline Arabic Handwriting Recognition Using Artificial Neural NetworkPreprint
Zaidan, A. A., Zaidan, B. B., Jalab, H. A., Alanazi, H. O., & Alnaqeib, R. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1006.2809 · Cited by 6
Fast Human Detection with Cascaded Ensembles
Bilgiç, B. (2010) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 6
LC‐YOLO: An Improved YOLOv8‐Based Lane Detection Model for Enhanced Lane Intrusion Detection
Abdullah, A., Guo, L., Al‐Soswa, M., & Debsi, A. (2025) · IET Image Processing · doi:10.1049/ipr2.70065 · Cited by 5
A Multi-View Pedestrian Tracking Framework Based on Graph Matching
Duanmu, F., Feng, X., Zhu, X., Tan, W.-T., & Wang, Y. (2018) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2018.00072 · Cited by 5
CISO: Co-iteration semi-supervised learning for visual object detection
Qi, J., Nguyen, M., & Yan, W. Q. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16915-4 · Cited by 5
Zhu, X., Li, B., Tao, J., & Jiang, B. (2015) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2015.7279388 · Cited by 5
Blur Classification Using Wavelet Transform and Feed Forward Neural Network
Tiwari, S., Shukla, V. P., Biradar, S. R., & Singh, A. K. (2014) · International Journal of Modern Education and Computer Science · doi:10.5815/ijmecs.2014.04.03 · Cited by 5
Juang, C., & Chang, S.-W. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.12.057 · Cited by 5
Efficiency improvement of human body detection with histograms of oriented gradients
Baranda, J., Jeanne, V., & Braspenning, R. (2008) · ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) · doi:10.1109/icdsc.2008.4635710 · Cited by 5
Spatial Relationship for Object Recognition
Zhu, L., & Yuan, H. (2008) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2008.386 · Cited by 5
Nishiyama, M., Nakano, S., Yotsumoto, T., Yoshimura, H., Iwai, Y., & Sugahara, K. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899943 · Cited by 5
System-on-chip-based hardware acceleration for human detection in 2D/3D scenes
Safaei, A., Wu, Q. M. J., & Akilan, T. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8122748 · Cited by 5
Visual Pedestrian Tracking from a UAV Platform
Zhang, L., Zhang, Z., & Xiong, H. (2017) · International Conference on Multimedia and Image Processing (ICMIP) · doi:10.1109/icmip.2017.53 · Cited by 5
Region of Interest-Based 3D Inpainting of Cultural Heritage Artifacts
Gangisetty, S., & Mudenagudi, U. (2018) · Journal on Computing and Cultural Heritage · doi:10.1145/3131778 · Cited by 5
Machine Learning for Image Based Motion Capture
Agarwal, A., Tel, H. I., De, D., Grenoble, P. D., Roger, M., Andrew, M., Pascal, M., William, M., Directeur, T., Philip, M., et al. (2006) · Cited by 5
Pedestrian detection from non-smooth motion
Kılıçarslan, M., Zheng, J. Y., & Algarni, A. (2015) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2015.7225732 · Cited by 5
Lavigne, D. A., Sahli, S., Ouyang, Y., & Sheng, Y. (2010) · International Conference on Information Fusion (FUSION) · doi:10.1109/icif.2010.5712007 · Cited by 5
Energy-transfer features and their application in the task of face detection
Fusek, R., Sojka, E., Mozdřeň, K., & Šurkala, M. (2013) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2013.6636631 · Cited by 5
An Improved Normalization Method for Haar-like Features for Real-time Object Detection
Park, K.-Y., & Hwang, S.-Y. (2011) · 한국통신학회논문지 · doi:10.7840/kics.2011.36c.8.505 · Cited by 5
Accuracy versus speed in context-based object detection
Bergboer, N. H., Postma, E. O., & van den Herik, H. J. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2006.08.004 · Cited by 5
NEW VEHICLE DETECTION ALGORITHM USING SYMMETRY SEARCH AND GA-BASED SVM
Truong, Q. B., Pham, V.-H., & Lee, B. R. (2013) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001413550033 · Cited by 5
A weighted image reconstruction based on PCA for pedestrian detection
Carvalho, G. V., de Moraes, L. B., Cavalcanti, G. D. C., & Ren, T. I. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033472 · Cited by 5
Extraction and classification of moving targets in multi-sensory MAMI-1 data collection
Ilin, R., & Clouse, H. S. (2015) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon.2015.7443102 · Cited by 5
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Bouma, H., Fischer, N. M., & Kruithof, M. C. (2017) · Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies · doi:10.1117/12.2277974 · Cited by 5
The Evolution of Contextual Information Processing in Informatics
Phivos Mylonas, B. (2018) · Information · doi:10.3390/info9030047 · Cited by 5
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Raman, R., Boubchir, L., Kumar, P., Majhi, B., & Bakshi, S. (2018) · Machine Vision and Applications · doi:10.1007/s00138-018-0939-6 · Cited by 5
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Fu, Z., Chen, Y., & Jiang, R. (2019) · IEEE Access · doi:10.1109/access.2019.2901270 · Cited by 5
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Liu, Z., Shi, Y., & Sun, M. (2018) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis.2018.8691384 · Cited by 5
Wang, A., Zhu, Y., Xu, Q., Wan, X., Yang, L., Fan, P., & Zhang, T. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3029556 · Cited by 5
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Krijeshan, G., Raghul, P., Nachiappan, N. N., Beulah, A., & Priyadharshini, R. A. (2021) · International Conference on Computer, Communication and Signal Processing (ICCCSP) · doi:10.1109/icccsp52374.2021.9465345 · Cited by 5
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Marappan, S., Kuppuswamy, P., John, R., & Shanmugavadivu, N. (2021) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-65407-8_33 · Cited by 5
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Arsić, D., Schuller, B. W., Hörnler, B., & Rigoll, G. (2009) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2009.5201162 · Cited by 5
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Tetik, Y. E., & Bölat, B. (2011) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista.2011.5946164 · Cited by 5
A robust approach for multi-human detection and tracking
Liu, X., Jin, Z., & Gao, M. (2012) · International Conference on Consumer Electronics, Communications and Networks (CECNet) · doi:10.1109/cecnet.2012.6201519 · Cited by 5
Xing, W., Bai, P., Zhang, S., & Bao, P. (2017) · Automatic Control and Computer Sciences · doi:10.3103/s0146411617030099 · Cited by 5
The OD theory of TOD: the use and limits of temporal information for object discovery
Sanders, B., Nelson, R. C., & Sukthankar, R. (2002) · doi:10.5555/777092.777212 · Cited by 5
Exploiting contextual data for event retrieval in surveillance video
Kelly, P., Conaire, C. Ó., & O'Connor, N. E. (2009) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1646396.1646437 · Cited by 4
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Boudissa, A., Tan, J. K., Hyoungseop, K. I. M., Shinomiya, T., & Ishikawa, S. (2013) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e96.d.2882 · Cited by 4
Fast pedestrian detection using BWLSD for ROI
Halidou, A., & You, X. (2013) · Wireless and Optical Communications Conference · doi:10.1109/wocc.2013.6676447 · Cited by 4
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Robust Pedestrian Detection via Curated Training on Created Dataset
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Villamizar, M., Sanfeliu, A., & Andrade‐Cetto, J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02172-5_18 · Cited by 4
Lee, H., & Banerjee, A. (2009) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2009.5346633 · Cited by 4
A stereo cameras setup for pedestrian detection enhancement
Nakamura, A. T. M., Horita, L. R. T., & Grassi, V. (2017) · IEEE International Symposium on Robotics and Intelligent Sensors (IRIS) · doi:10.1109/iris.2017.8250147 · Cited by 4
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Onesimu, J. A., Sebastian, R. D., Sei, Y., & Christopher, L. (2021) · Annals of Emerging Technologies in Computing · doi:10.33166/aetic.2021.04.003 · Cited by 4
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On-road vehicle detectioin using histograms of multi-scale orientations
Kong, F., Ye, Q., Zhang, N., Lu, K., & Jiao, J. (2009) · IEEE Youth Conference on Information, Computing and Telecommunication (YC-ICT) · doi:10.1109/ycict.2009.5382389 · Cited by 4
Feature subset selection based on co-evolution for pedestrian detection
Cao, X., Xu, Y., Wei, C. X., & Guo, Y. (2010) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331209103041 · Cited by 4
Htike, K. K., & Hogg, D. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025474 · Cited by 4
Farhadi, M., Motamedi, S. A., & Sharifian, S. (2011) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/iranianmvip.2011.6121596 · Cited by 4
Wang, H., Wang, B., & Li, S. (2013) · International Conference on Image and Graphics · doi:10.1109/icig.2013.70 · Cited by 4
Li, M., Hu, Q., Wang, Y., & Dong, W. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.08.025 · Cited by 4
Automatic object detection and matching based on proposed signature
Elsalamony, H. A. (2012) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2012.6376588 · Cited by 4
Vehicle Detection Using Local Size-Specific Classifiers
Noh, S., & Jeon, M. (2016) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2016edp7101 · Cited by 4
Novel vehicle detection system based on stacked DoG kernel and AdaBoost
Kang, H. H., Lee, S. W., You, S. H., & Ahn, C. K. (2018) · PLoS ONE · doi:10.1371/journal.pone.0193733 · Cited by 4
Decade of Vision-Based Pedestrian Detection for Self-Driving: An Experimental Survey and Evaluation
Zhang, Y., Qi, Y., Liu, J., & Wang, Y. (2018) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2018-01-1603 · Cited by 4
Special Pedestrian and Head Pose Detection for Autonomous Vehicles
Shetty, S. S., Meena, S. M., Kulkarni, U., & Hebballi, H. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-2553-3_18 · Cited by 4
Multi-camera Torso Pose Estimation using Graph Neural Networks
Rodriguez-Criado, D., Bachiller, P., Bustos, P., Vogiatzis, G., & Manso, L. J. (2020) · IEEE RO-MAN · doi:10.1109/ro-man47096.2020.9223542 · Cited by 4
A Study and Analysis on Pedestrian Detection and Tracking Through Rear-View Images
Lakshmi, D. S., Divya, A., Sreedevi, E., Ravikiran, K., Gottumukkala, P., & Muddana, A. (2023) · Ingénierie des systèmes d information · doi:10.18280/isi.280103 · Cited by 4
Big Data Approach for Fire Prevention and Warning for Power Systems
Li, Z., Zhang, X., & Wang, H. (2023) · Journal of Signal Processing Systems · doi:10.1007/s11265-023-01857-9 · Cited by 4
Monocular Camera Ranging based on Vehicle Attitude Estimation
Li, B., Song, Z., & Guo, X. (2022) · International Conference on Information Systems and Computing Technology (ISCTech) · doi:10.1109/isctech58360.2022.00122 · Cited by 4
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 ›
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