Unified crowd segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5305 LNCS, Issue PART 4, 2008, Pages 691-704

  Tu, Peter ^a   Sebastian, Thomas ^a   Doretto, Gianfranco ^a   Krahnstoever, Nils ^a   Rittscher, Jens ^a   Yu, Ting ^a  

^a GE GLOBAL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MAXIMUM PRINCIPLE;

APPEARANCE MODELS; EXPECTATION-MAXIMIZATION FRAMEWORKS; GLOBAL CONSISTENCY; GLOBAL SOLUTIONS; LOW-LEVEL IMAGE FEATURES; OCCLUSION REASONING; PAIRWISE LIKELIHOOD; PARTIAL OCCLUSIONS;

COMPUTER VISION;

EID: 57149140231     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88693-8_51     Document Type: Conference Paper

References (20)

1. Munder, S., Gavrila, D.: An experimental study on pedestrian classification. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(11), 1863-1868 (2006)
2. Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. IEEE Computer Vision and Pattern Recognition, 878-885 (2005)
3. Leibe, B., Cornelis, N., Cornelis, K., Gool, L.V.: Dynamic 3d scene analysis from a moving vehicle. IEEE Computer Vision and Pattern Recognition, 1-8 (2007)
4. Rittscher, J., Tu, P.H., Krahnstoever, N.: Simultaneous estimation of segmentation and shape. IEEE Computer Vision and Pattern Recognition 2, 486-493 (2005)
5. Brostow, G.J., Cipolla, R.: Unsupervised bayesian detection of independent motion in crowds. IEEE Computer Vision and Pattern Recognition I, 594-601 (2006)
6. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. IEEE Computer Vision and Pattern Recognition, 886-893 (2005)
7. Tuzel, O., Porikli, F., Meer, P.: Pedestrian detection via classification on riemannian manifolds. IEEE Computer Vision and Pattern Recognition (2007)
8. Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. IEEE Computer Vision and Pattern Recognition 2, 246-252 (1998)
9. Rabaud, V., Belongie, S.: Counting crowded moving objects. IEEE Computer Vision and Pattern Recognition, 705-711 (2006)
10. Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision 2, 734-741 (2003)
11. Fergus, R., Perona, P., Zisserman, A.: A visual category filter for Google images. In: European Conference on Computer Vision, vol. 1, pp. 242-256 (2004)
12. Mikolajczyk, K., Schmid, C., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detectors. In: European Conference on Computer Vision (2004)
13. Wu, B., Nevatia, R.: Detection and tracking of multiple partially occluded humans by bayesian combination of edgelet based part detectors. International Journal of Computer Vision 75(2), 247-266 (2007)
14. Zhao, T., Nevatia, R.R.: Bayesian human segmentation in crowded situations. IEEE Computer Vision and Pattern Recognition 2, 459-466 (2003)
15. Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding 89(3), 114-141 (2003)
16. Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137-154 (2004)
17. Krahnstoever, N., Tu, P., Sebastian, T., Perera, A., Collins, R.: Multi-view detection and tracking of travelers and luggage in mass transit environments. In: Proc. Ninth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS) (2006)
18. Leibe, B., Schindler, K., Gool, L.V.: Coupled detection and trajectory estimation for multi-object tracking. In: International Conference on Computer Vision (ICCV 2007), Rio de Janeiro, Brasil (October 2007)
19. Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House Publishers (1999)
20. Rasmussen, C., Hager, G.: Joint probabilistic techniques for tracking multi-part objects. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 16-21 (1998)