Video tracking is the process of localizing an object in video at every time instant. It is a challenging problem especially when there are multiple objects to follow, and they may look similar and overlap as they move or deform over time. A particular scenario is given by people tracking across a network of multiple cameras. People need to first be detected (Perera et al., 2006), potentially against a dynamic background (Kim et al., 2009), and then segmented while accounting for occlusions (Tu et al., 2008). If the camera system is fully calibrated, the detections from multiple cameras can be backprojected to a planar location and integrated for occlusion resolution and temporal filtering (Krahnstoever et al., 2009) (Tu et al., 2007). Tracking information can then be linked across cameras and temporal gaps with person re-identification (Tu et al., 2011) (Doretto et al., 2011).

People Detection

3 minute read

People detection and tracking in video are fundamental Computer Vision capabilities that still constitute a research challenge. Important difficulties are du...

Video Surveillance

7 minute read

Increasingly, large networks of surveillance cameras are employed to monitor public and private facilities. This continuous collection of imagery has the pot...

Video Understanding

9 minute read

Video understanding is concerned with the parsing of the image data flow for the semantic understanding of the objects in the scene, but also their actions a...


  1. JAIHC
    Appearance-based person reidentification in camera networks: problem overview and current approaches Doretto, G., Sebastian, T., Tu, P., and Rittscher, J. Journal of Ambient Intelligence and Humanized Computing, 2011. abstract bibTeX pdf html
  2. Video Analytics for Force Protection Tu, P.H., Brooksby, G.W., Doretto, G., Hamilton, D.W., Krahnstoever, N., Laflan, J.B., Liu, X., Patwardhan, K.A., Sebastian, T., Tong, Y., Tu, J., Wheeler, F.W., Wynnyk, C. M., Yao, Y., and Yu, T. In Distributed Video Sensor Networks, 2011. abstract bibTeX pdf html
  3. AVSS
    A model change detection approach to dynamic scene modeling Kim, S. J., Doretto, G., Rittscher, J., Tu, P., Krahnstoever, N., and Pollefeys, M. In IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009. Oral abstract bibTeX pdf
  4. AVSS
    Intelligent video for protecting crowded sports venues Krahnstoever, N., Tu, P., Yu, T., Patwardhan, K., Hamilton, D., Yu, B., Greco, C., and Doretto, G. In IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009. (Invited paper) Oral bibTeX pdf
  5. ECCV
    Unified crowd segmentation Tu, P., Sebastian, T., Doretto, G., Krahnstoever, N., Rittscher, J., and Yu, T. In Proceedings of European Conference on Computer Vision, 2008. abstract bibTeX pdf
    An intelligent video framework for homeland protection Tu, P. H., Doretto, G., Krahnstoever, N. O., Perera, A. A. G., Wheeler, F. W., Liu, X., Rittscher, J., Sebastian, T. B., Yu, T., and Harding, K. G. In Proceedings of SPIE Defence and Security Symposium - Unattended Ground, Sea, and Air Sensor Technologies and Applications IX, 2007. (Invited paper) abstract bibTeX pdf
  7. CVPRW
    Moving object segmentation using scene understanding Perera, A. G. A., Brooksby, G., Hoogs, A., and Doretto, G. In Proceedings of IEEE Computer Society Workshop on Perceptual Organization in Computer Vision, 2006. abstract bibTeX pdf doi