Towards unlocking web video: Automatic people tracking and clustering

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009

Volumn , Issue , 2009, Pages 47-54

  Holub, Alex ^a   Moreels, Pierre ^a   Islam, Atiq ^a   Makhanov, Andrei ^a   Yang, Rui ^a  

^a Ooyala Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MULTIMEDIA SYSTEMS;

BROAD SPECTRUM; GROUND TRUTH DATA; META INFORMATION; PEOPLE TRACKING; ROBUST TRACKING; SHOT DETECTION; VIDEO CONTENTS; WEB CONTENT;

COMPUTER VISION;

EID: 70449556250     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2009.5204190     Document Type: Conference Paper

References (16)

1. D. Comaniciu, V. Ramesh, and P. Meer. Real-time tracking of non-rigid objects using mean shift. CVPR, 2:142-149, 2000. 3
2. J. Dean and S. Ghemawat. Mapreduce: Simplified data processing on large clusters. OSDI'04: Sixth Symposium on Operating System Design and Implementation, 2004. 7
3. M. Everingham, J. Sivic, and A. Zisserman. Hello! my name is... buffy - automatic naming of characters in tv video. Proceedings of the British Machine Vision Conference, 2006. 2
4. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2008 (VOC2008) Results. http://www.pascalnetwork.org/challenges/VOC/voc2008/workshop/index.html. 3
5. R. Fergus, A. Zisserman, and P. Perona. Learning object categories from google's image search. ICCV, 2:1816-1823, 2005. 1
6. Google. Google image search, http://images.google.com/. 1
7. G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology, 2007. 1
8. C. Manning, P. Raghavan, and H. Schutze. Introduction to information retrieval. 2008. 5
9. S. Milborrow and F. Nicolls. Locating facial features with an extended active shape model. ECCV, pages 1746-1759, 2008. 4
10. T. Pappas. An adaptive clustering algorithm for image segmentation. IEEE Transactions on Signal Processing, 40:901-914, 1992. 5
11. J. Ponce, T. L. Berg, M. Everingham, D. A. Forsyth, M. Hebert, S. Lazebnik, M. Marszalek, C. Schmid, B. C. Russell, A. Torralba, C. K. I. Williams, J. Zhang, and A. Zisserman. Dataset issues in object recognition. In J. Ponce, M. Hebert, C. Schmid, and A. Zisserman, editors, Toward Category-Level Object Recognition, volume 4170 of LNCS, pages 29-48. Springer, 2006. 1
12. F. Porikli. Integral histogram: A fast way to extract histograms. CVPR, 1:829-836, 2005. 3
13. H. Schneiderman. Feature-centric evaluation for efficient cascaded object detection. CVPR, 2004. 2
14. H. Schneiderman and T. Kanadě. A statistical method for 3d object detection applied to faces and cars. CVPR, pages 1746-1759, 2000. 2
15. A. Torralba, R. Fergus, and W. T. Freeman. 80 million tiny images: a large data-set for non-parametric object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30:1958-1970, 2008. 1
16. P. Viola and M. Jones. Robust real-time object detection. Internationaljournal of Computer Vision, 2001. 1, 2