People Detection

Summary

People detection and tracking in video are fundamental Computer Vision capabilities that still constitute a research challenge. Important difficulties are due to the partial occlusions of the objects of interest (people), the dynamic background (possibly due to the motion of the observer), and the foreground clutter (due to non-person objects in motion). Traditional methods ignore one or more of these aspects, and this prevents from enabling tracking multiple people from a moving platform, even in slightly crowded or cluttered conditions. In (Tu et al., 2008) these issues are addressed all at once by exploiting both people appearance and shape cues in an online optimization framework based on Expectation Maximization. Given initial hypothesis of people positions nominated by a discriminative head and shoulder detector, operating at a high false alarm rate, images are analyzed by optimally assigning each image patch to the most likely person hypothesis. This amounts to automatically reject the false hypothesis, find how many people are present in the scene, localize them, and describe how they occlude each other.

References

1. 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

   This paper presents a unified approach to crowd segmentation. A global solution is generated using an Expectation Maximization framework. Initially, a head and shoulder detector is used to nominate an exhaustive set of person locations and these form the person hypotheses. The image is then partitioned into a grid of small patches which are each assigned to one of the person hypotheses. A key idea of this paper is that while whole body monolithic person detectors can fail due to occlusion, a partial response to such a detector can be used to evaluate the likelihood of a single patch being assigned to a hypothesis. This captures local appearance information without having to learn specific appearance models. The likelihood of a pair of patches being assigned to a person hypothesis is evaluated based on low level image features such as uniform motion fields and color constancy. During the E-step, the single and pairwise likelihoods are used to compute a globally optimal set of assignments of patches to hypotheses. In the M-step, parameters which enforce global consistency of assignments are estimated. This can be viewed as a form of occlusion reasoning. The final assignment of patches to hypotheses constitutes a segmentation of the crowd. The resulting system provides a global solution that does not require background modeling and is robust with respect to clutter and partial occlusion.

   @inproceedings{tuSDKRY08eccv,
     abbr = {ECCV},
     author = {Tu, P. and Sebastian, T. and Doretto, G. and Krahnstoever, N. and Rittscher, J. and Yu, T.},
     title = {Unified crowd segmentation},
     booktitle = {Proceedings of European Conference on Computer Vision},
     year = {2008},
     pages = {691--704},
     bib2html_pubtype = {Conferences},
     bib2html_rescat = {Video Analysis, People Detection, Integral Image Computations,
     	People Tracking},
     file = {tuSDKRY08eccv.pdf:doretto\\conference\\tuSDKRY08eccv.pdf:PDF},
     owner = {doretto},
     timestamp = {2008.01.16}
   }