The goal of this work is to provide a system which can aid in monitoring crowded urban environments, which often contain tight groups of people. In this paper, we consider the problem of counting the number of people in the scene and also tracking them reliably. We propose a novel method for detecting and estimating the count of people in groups, dense or otherwise, as well as tracking them. Using prior knowledge obtained from the scene and accurate camera calibration, the system learns the parameters required for estimation. This information can then be used to estimate the count of people in the scene, in real-time. Groups are tracked in the same manner as individuals, using Kalman filtering techniques. Favorable results are shown for groups of various sizes moving in an unconstrained fashion.
Bibliographical noteFunding Information:
This work has been supported in part by Johnson Controls Inc., the ITS Institute at the University of Minnesota, and the National Science Foundation through Grants IIS-0219863 and IIP-0443945.
- Count estimation
- Pedestrian tracking