Large-scale vehicle detection in challenging urban surveillance environments

2011 IEEE Workshop on Applications of Computer Vision, WACV 2011

Volumn , Issue , 2011, Pages 527-533

  Feris, Rogerio ^a   Petterson, James ^b   Siddiquie, Behjat ^c   Brown, Lisa ^a   Pankanti, Sharath ^a  

^a IBM RESEARCH   (United States)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CO-TRAINING; ENVIRONMENTAL CONDITIONS; FEATURE SELECTION; IMAGE GRADIENTS; LARGE OCCLUSION; MULTIPLE FEATURES; OCCLUSION HANDLING; QUANTITATIVE ANALYSIS; TRAINING SAMPLE; URBAN SURVEILLANCE; VEHICLE DETECTION;

COMPUTER VISION; DETECTORS; FEATURE EXTRACTION; IMAGE RECONSTRUCTION; LAPTOP COMPUTERS; MONITORING; SECURITY SYSTEMS;

COMPUTER APPLICATIONS;

EID: 79952497189     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WACV.2011.5711549     Document Type: Conference Paper

References (20)

1. S. Agarwal, A. Awan, and D. Roth. Learning to detect objects in images via a sparse, part-based representation. IEEE Transactions on PAMI, 26(11), 2004.
2. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
3. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html.
4. R. Fattal, D. Lischinski, and M. Werman. Gradient domain high dynamic range. In ACM SIGGRAPH, 2002.
5. P. Felzenszwalb and R. G. and D. McAllester. Cascade object detection with deformable part models. In CVPR, 2010.
6. G. Griffin, A. D. Holub, and P. Perona. The caltech-256. Caltech Technical Report.
7. A. Hampapur, L. Brown, R. S. Feris, A. Senior, C. Shu, Y. Tian, Y. Zhai, and M. Lu. Searching surveillance video. In AVSS, 2007.
8. C. Kuo and R. Nevatia. Robust multi-view car detection using unsupervised sub-categorization. WACV, 2009.
9. Y. Lin and T. Liu. Fast object detection with occlusions. In ECCV, 2004.
10. S. Merler, B. Caprile, and C. Furlanello. Parallelizing Ad-aBoost by weights dynamics. Computational Statistics & Data Analysis, 51(5):2487-2498, 2007.
11. S. A. O. Javed and M. Shah. Online detection and classification of moving objects using progressively improving detectors. In CVPR, 2005.
12. P. Roth, H. Grabner, D. Skocaj, H. Bischol, and Leonardis. On-line conservative learning for person detection. In PETS Workshop, 2005.
13. O. Russakovsky and L. Fei-Fei. Attribute learning in large-scale datasets. In ECCV 2010 Workshop on Parts and Attributes, 2010.
14. H. Schneiderman and T. Kanade. A statistical approach to 3D object detection applied to faces and cars. In CVPR, 2000.
15. A. Senior, A. Hampapur, Y.-L. Tian, L. Brown, S. Pankanti, and R. Bolle. Appearance models for occlusion handling. Journal of Image and Vision Computing, 24(11), 2006.
16. C. Stauffer and W. Grimson. Adaptive background mixture models for real-time tracking. In CVPR, 1998.
17. Y. Tian, M. Lu, and A. Hampapur. Robust and efficient foreground analysis for real-time video surveillance. In CVPR, 2005.
18. P. Viola and M. Jones. Robust Real-time Object Detection. In International Journal of Computer Vision, 2001.
19. B. Wu and R. Nevatia. Cluster boosted tree classifier for multi-view, multi-pose object detection. In ICCV, 2007.
20. W. Zheng and L. Liang. Fast car detection using image strip features. In CVPR, 2009.