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
Cited by
Sorted by citations
Items 301–400 of 1,000
High-Level Fusion of Depth and Intensity for Pedestrian Classification
Rohrbach, M., Enzweiler, M., & Gavrila, D. M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03798-6_11 · Cited by 33
Pedestrian Detection Using SVM and Multi-Feature Combination
Sotelo, M. Á., Parra, I., Fernandez, D., & Naranjo, E. (2006) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2006.1706726 · Cited by 33
Pure FPGA Implementation of an HOG Based Real-Time Pedestrian Detection System
Luo, J., & Lin, C. H. (2018) · Sensors · doi:10.3390/s18041174 · Cited by 32
People detection in low resolution infrared videos
Miezianko, R., & Pokrajac, D. (2008) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4563056 · Cited by 32
Pedestrian Detection Using Boosted HOG Features
Wang, Z., Jia, Y., Huang, H., & Tang, S. (2008) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2008.4732553 · Cited by 32
Guo, X., Liu, D., Jou, B., Zhu, M., Cai, A., & Chang, S. (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.2013.412 · Cited by 31
A novel quality assessment for visual secret sharing schemes
Jiang, F., & King, B. (2017) · EURASIP Journal on Information Security · doi:10.1186/s13635-016-0053-0 · Cited by 31
Vision Based Intelligent Traffic Management System
Malhi, M. H., Aslam, M. H., Saeed, F., Javed, O., & Fraz, M. M. (2011) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit.2011.33 · Cited by 31
Boosting object detection using feature selection
Sun, Z., Bebis, G. N., & Miller, R. H. (2004) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2003.1217934 · Cited by 31
Syncretic-NMS: A Merging Non-Maximum Suppression Algorithm for Instance Segmentation
Chu, J., Zhang, Y., Li, S., Leng, L., & Miao, J. (2020) · IEEE Access · doi:10.1109/access.2020.3003917 · Cited by 30
An interpretable deep neural network for colorectal polyp diagnosis under colonoscopy
Wang, S., Yin, Y., Wang, D., Lv, Z., Wang, Y., & Jin, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107568 · Cited by 30
Visual Relationship Detection: A Survey
Cheng, J., Wang, L., Wu, J., Hu, X., Jeon, G., Tao, D., & Zhou, M. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3142013 · Cited by 30
Computer Vision Approaches to Pedestrian Detection: Visible Spectrum Survey
Gerónimo, D., López, A. M., & Sappa, A. D. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72847-4_70 · Cited by 30
Vision-Based System for Human Detection and Tracking in Indoor Environment
Benezeth, Y., Emile, B., Laurent, H., & Rosenberger, C. (2009) · International Journal of Social Robotics · doi:10.1007/s12369-009-0040-4 · Cited by 30
A review of object representation based on local features
Cao, J., Mao, D., Cai, Q., Li, H., & Du, J. (2013) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/jzus.cide1303 · Cited by 29
Vision-Based Human Detection Techniques: A Descriptive Review
Sumit, S. S., Rambli, D. R. A., & Mirjalili, S. (2021) · IEEE Access · doi:10.1109/access.2021.3063028 · Cited by 29
Multi-stage Sampling with Boosting Cascades for Pedestrian Detection in Images and Videos
Gualdi, G., Prati, A., & Cucchiara, R. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3_15 · Cited by 29
A Regularized Framework for Feature Selection in Face Detection and Authentication
Destrero, A., Mol, C. D., Odone, F., & Verri, A. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0180-2 · Cited by 29
Vehicle detection based on morphology from highway aerial images
Zheng, Z., Wang, X., Zhou, G., & Jiang, L. L. (2012) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2012.6352241 · Cited by 29
Multiple Instance Boost Using Graph Embedding Based Decision Stump for Pedestrian Detection
Pang, J., Huang, Q., & Jiang, S. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88693-8_40 · Cited by 29
Kim, S.-Y., Kang, J.-K., Oh, S., Ryu, Y.-W., Kim, K., Park, S., & Kim, J. (2007) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-007-9187-0 · Cited by 28
Hausdorff Kernel for 3D Object Acquisition and Detection
Barla, A., Odone, F., & Verri, A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47979-1_2 · Cited by 28
Alternating projection method for a class of tensor equations
Li, Z., Dai, Y., & Gao, H. (2018) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2018.07.013 · Cited by 27
Aumann, Y., Feldman, R., Liberzon, Y., Rosenfeld, B. L., & Schler, J. (2006) · Knowledge and Information Systems · doi:10.1007/s10115-006-0014-x · Cited by 27
Vision-Based People Detection System for Heavy Machine Applications
Frémont, V., Bui, M. T., Boukerroui, D., & Letort, P. (2016) · Sensors · doi:10.3390/s16010128 · Cited by 27
Histogram of oriented phase (HOP): a new descriptor based on phase congruency
Ragb, H. K., & Asari, V. K. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2225159 · Cited by 27
Pedestrian Detection and Tracking Using HOG and Oriented-LBP Features
Ma, Y., Chen, X., & Chen, G. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24403-2_15 · Cited by 27
Target recognition by components for mobile robot navigation
Cicirelli, G., D’Orazio, T., & Distante, A. (2003) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813021000039430 · Cited by 27
Pedestrian Detection for a Near Infrared Imaging System
Soga, M., Hiratsuka, S., Fukamachi, H., & Ninomiya, Y. (2008) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2008.4732710 · Cited by 27
Visual recognition and detection under bounded computational resources
Vijayanarasimhan, S., & Kapoor, A. (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.2010.5540109 · Cited by 27
Şentaş, A., Kul, S., & Sayar, A. (2019) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap.2019.8875889 · Cited by 26
Robust Real‐Time Traffic Surveillance with Deep Learning
Fernández, J., Cañas, J. M., Fernández, V., & Paniego, S. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/4632353 · Cited by 26
Integrating Orientation Cue With EOH-OLBP-Based Multilevel Features for Human Detection
Ma, Y., Deng, L., Chen, X., & Guo, N. (2013) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2013.2268991 · Cited by 26
Hong, G.-S., Kim, B., Hwang, Y.-S., & Kwon, K.-K. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2455-2 · Cited by 26
Granularity-tunable gradients partition (GGP) descriptors for human detection
Liu, Y., Shan, S., Zhang, W., Chen, X., & Gao, W. (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.2009.5206724 · Cited by 26
A Cascade of Feed-Forward Classifiers for Fast Pedestrian Detection
Chen, Y., & Chen, C. (2007) · Springer eBooks · doi:10.1007/978-3-540-76386-4_86 · Cited by 26
Evolutionary Gabor filter optimization with application to vehicle detection
Sun, Z., Bebis, G. N., & Miller, R. H. (2004) · Proceedings · doi:10.1109/icdm.2003.1250934 · Cited by 26
Heisele, B., Verri, A., & Poggio, T. (2002) · Proceedings of the IEEE · doi:10.1109/jproc.2002.801450 · Cited by 26
Self-citationObject Localization Based on Proposal Fusion
Tang, S., Li, Y., Deng, L., & Zhang, Y. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2729786 · Cited by 25
A survey of deep learning techniques for detecting and recognizing objects in complex environments
Dogra, A. K., Sharma, V., & Sohal, H. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100686 · Cited by 25
A Pedestrian-Detection Method Based on Heterogeneous Features and Ensemble of Multi-View–Pose Parts
Liu, W., Yu, B., Duan, C., Chai, L., Yuan, H., & Zhao, H. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2342936 · Cited by 25
Lim, J.-S., & Kim, W. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1156-3 · Cited by 25
Seamless Insertion of Pulmonary Nodules in Chest CT Images
Pezeshk, A. X., Sahiner, B., Zeng, R., Wunderlich, A., Chen, W., & Petrick, N. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2445054 · Cited by 25
Shirazi, M. S., & Morris, B. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14249-4_68 · Cited by 25
Robust real-time pedestrian detection in surveillance videos
Varga, D., & Szirányi, T. (2016) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-016-0369-0 · Cited by 25
Generating Easy-to-Understand Referring Expressions for Target Identifications
Tanaka, M. N., Itamochi, T., Narioka, K., Sato, I., Ushiku, Y., & Harada, T. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00589 · Cited by 25
An integrated framework of vision-based vehicle detection with knowledge fusion
Zhu, Y., Comaniciu, D., Ramesh, V., Pellkofer, M., & Koehler, T. (2005) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2005.1505102 · Cited by 25
Human Detection Using Learned Part Alphabet and Pose Dictionary
Yao, C., Bai, X., Liu, W., & Latecki, L. J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10602-1_17 · Cited by 24
Cross-Articulation Learning for Robust Detection of Pedestrians
Seemann, E., & Schiele, B. (2006) · Lecture notes in computer science · doi:10.1007/11861898_25 · Cited by 24
Multi-class Object Detection in Vision Systems Using a Hierarchy of Cascaded Classifiers
Kallenbach, I., Schweiger, R., Palm, G., & Löhlein, O. (2006) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2006.1689658 · Cited by 24
A Study of Feature Combination for Vehicle Detection Based on Image Processing
Arróspide, J., & Salgado, L. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/196251 · Cited by 24
Fast detection of human using differential evolution
Chen, N., Chen, W., & Zhang, J. (2014) · Signal Processing · doi:10.1016/j.sigpro.2014.08.044 · Cited by 24
Semantic image classification using statistical local spatial relations model
Han, D., Li, W., & Li, Z. (2008) · Multimedia Tools and Applications · doi:10.1007/s11042-008-0203-6 · Cited by 24
Fast Human Detection Using Motion Detection and Histogram of Oriented Gradients
Beiping, H., & Zhu, W. Q. (2011) · Journal of Computers · doi:10.4304/jcp.6.8.1597-1604 · Cited by 24
Robust Vehicle Detection for Tracking in Highway Surveillance Videos Using Unsupervised Learning
Tamersoy, B., & Aggarwal, J. K. (2009) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2009.57 · Cited by 24
Front and Rear Vehicle Detection Using Hypothesis Generation and Verification
Khairdoost, N., Amirhassan Monadjemi, S., & Jamshidi, K. (2013) · Signal & Image Processing An International Journal · doi:10.5121/sipij.2013.4403 · Cited by 24
Detecting Comma-Shaped Clouds for Severe Weather Forecasting Using Shape and Motion
Zheng, X., Ye, J., Chen, Y., Wistar, S., Li, J., Piedra-Fernandez, J. A., Steinberg, M. A., & Wang, J. Z. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2887206 · Cited by 24
Novel and efficient pedestrian detection using bidirectional PCA
Nguyen, T.-H.-B., & Kim, H. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.01.007 · Cited by 23
Fast human detection based on parallelogram haar-like features
Hoang, V.-D., Vavilin, A., & Jo, K.-H. (2012) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2012.6389212 · Cited by 23
Khan, T., Zeeshan, A., & Dougherty, M. S. (2021) · Technology and Health Care · doi:10.3233/thc-191960 · Cited by 23
Vehicle Detection Using Mixture of Deformable Parts Models: Static and Dynamic Camera
Leon, L. M. C., & Hirata, R. (2012) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2012.40 · Cited by 23
Automated Insect Identification through Concatenated Histograms of Local Appearance Features
Larios, N., Deng, H., Zhang, W., Sarpola, M. J., Yuen, J., Paasch, R., Moldenke, A. R., Lytle, D. A., Ruíz-Correa, S., Mortensen, E. N., et al. (2007) · Proceedings · doi:10.1109/wacv.2007.13 · Cited by 23
Multi-Level Haar Wavelet based Facial Expression Recognition using Logistic Regression
Goyani, M. M., & Patel, N. M. (2017) · Indian Journal of Science and Technology · doi:10.17485/ijst/2017/v10i9/108944 · Cited by 23
Multi-Camera Person Tracking and Left Luggage Detection Applying Homographic Transformation
Arsić, D., Hofmann, M. R., Schuller, B. W., & Rigoll, G. (2007) · Cited by 23
Object Categorization via Local Kernels
Barbara Caputo, B., Wallraven, C., & Nilsback, M.-E. (2004) · Cited by 23
Berg, T. L., Sorokin, A. A., Wang, G., Forsyth, D. A., Hoiem, D., Endres, I., & Farhadi, A. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2032355 · Cited by 23
Improved feature reduction in input and feature spaces
Shih, F. Y., & Cheng, S. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2004.10.004 · Cited by 23
Deep Learning Approach to Key Frame Detection in Human Action Videos
Gawande, U. H., Hajari, K. O., & Golhar, Y. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.91188 · Cited by 22
A System for Assisting the Visually Impaired in Localization and Grasp of Desired Objects
Thakoor, K. A., Mante, N. T., Zhang, C., Siagian, C., Weiland, J. D., Itti, L., & Medioni, G. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16199-0_45 · Cited by 22
Real-Time Pedestrian Detection for Autonomous Driving
Yang, Z., Li, J., & Li, H. (2018) · Intelligent Autonomous Systems · doi:10.1109/icoias.2018.8494031 · Cited by 21
Detecting pedestrians on a Movement Feature Space
Negri, P., Goussies, N. A., & Lotito, P. A. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.05.020 · Cited by 21
Vision-based 3D bicycle tracking using deformable part model and Interacting Multiple Model filter
Cho, H., Rybski, P. E., & Zhang, W. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980482 · Cited by 21
Stereo-based Pedestrian Detection using Multiple Patterns
Hattori, H., Seki, A., Nishiyama, M., & Watanabe, T. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.94 · Cited by 21
Multi-scale graph-transformer network for trajectory prediction of the autonomous vehicles
Singh, D., & Srivastava, R. (2022) · Intelligent Service Robotics · doi:10.1007/s11370-022-00422-w · Cited by 21
Database-Guided Simultaneous Multi-slice 3D Segmentation for Volumetric Data
Hong, W., Georgescu, B., Zhou, X. S., Krishnan, S., Ma, Y., & Comaniciu, D. (2006) · Lecture notes in computer science · doi:10.1007/11744085_31 · Cited by 21
Fernandes, S. L., & Bala, J. G. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-13728-5_13 · Cited by 20
Das, H. S., Das, A., Neog, A., Mallik, S., Bora, K., & Zhao, Z. (2022) · Mathematics · doi:10.3390/math10224218 · Cited by 20
CPPE-5: Medical Personal Protective Equipment Dataset
Dagli, R., & Shaikh, A. M. (2023) · SN Computer Science · doi:10.1007/s42979-023-01748-7 · Cited by 20
Variational learning of finite Dirichlet mixture models using component splitting
Fan, W., Bouguila, N., & Ziou, D. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.03.049 · Cited by 20
Extended Histogram of Gradients feature for human detection
Satpathy, A., Jiang, X., & Eng, H. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5650070 · Cited by 20
Overtaking vehicle detection using a spatio-temporal CRF
Zhang, X., Jiang, P., & Wang, F. (2014) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2014.6856546 · Cited by 20
Three-level cascade of random forests for rapid human detection
Ko, B. C., Kim, D.-Y., Jung, J., & Nam, J.-Y. (2013) · Optical Engineering · doi:10.1117/1.oe.52.2.027204 · Cited by 20
Sensor Fusion Based Obstacle Detection/Classification for Active Pedestrian Protection System
Jung, H. G., Lee, Y., Yoon, P. J., Hwang, I. Y., & Kim, J. (2006) · Lecture notes in computer science · doi:10.1007/11919629_31 · Cited by 20
Visual Object Detection Using Cascades of Binary and One-Class Classifiers
Çevıkalp, H., & Triggs, B. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0986-2 · Cited by 20
Fast pedestrian detection using multimodal estimation of distribution algorithms
Tan, D.-Z., Chen, W., Zhang, J., & Yu, W. (2017) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3071178.3071237 · Cited by 20
Sequential Architecture for Efficient Car Detection
Zhu, Z., Zhao, Y., & Lu, H. (2007) · 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.2007.383499 · Cited by 20
Learning Algorithm for Real-Time Vehicle Tracking
Withopf, D., & Jähne, B. (2006) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2006.1706793 · Cited by 20
Kim, S., & Cho, K. (2014) · doi:10.6688/jise.2014.30.6.3 · Cited by 20
Related HOG Features for Human Detection Using Cascaded Adaboost and SVM Classifiers
Liu, H., Xu, T., Wang, X., & Qian, Y. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-35728-2_33 · Cited by 20
Computer Vision and Machine Learning for Enhancing Pedestrian Safety
Gandhi, T., & Trivedi, M. M. (2008) · Studies in computational intelligence · doi:10.1007/978-3-540-79257-4_4 · Cited by 20
Improved Haar Wavelet Feature Extraction Approaches for Vehicle Detection
Wen, X., Yuan, H., Yang, C., Song, C., Duan, B., & Zhao, H. (2007) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2007.4357743 · Cited by 20
Malagoli, E., & Persio, L. D. (2025) · Mathematics · doi:10.3390/math13060893 · Cited by 19
Cascade Classifiers and Saliency Maps Based People Detection
Aguilar, W. G., Luna, M. A., Moya, J. F., Abad, V., Ruiz, H., Parra, H., & Lopez, W. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-60928-7_42 · Cited by 19
Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
Yang, L., Liu, S., & Zhao, Y. (2022) · Applied Sciences · doi:10.3390/app12073322 · Cited by 19
Shared Features for Multiclass Object Detection
Torralba, A. B., Murphy, K. P., & Freeman, W. T. (2006) · Lecture notes in computer science · doi:10.1007/11957959_18 · Cited by 19
Tracking and Motion Cues for Rear-View Pedestrian Detection
Levi, D., & Silberstein, S. (2015) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2015.114 · Cited by 19
Reliable pedestrian recognition combining high-definition LIDAR and vision data
Kidono, K., Naito, T., & Miura, J. (2012) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2012.6338657 · Cited by 19
Pedestrian detection algorithm for on-board cameras of multi view angles
Kamijo, S., Fujimura, K., & Shibayama, Y. (2010) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2010.5548113 · Cited by 19
Detection of objects carried by people
Branca, A., Leo, M., Attolico, G., & Distante, A. (2003) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2002.1038969 · Cited by 19
Monitoring activity of taking medicine by incorporating RFID and video analysis
Hasanuzzaman, F. M., Yang, X., Tian, Y., Liu, Q., & Capezuti, E. (2013) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-013-0025-y · Cited by 19
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