Abstract
In this paper, we propose a vision-based system for human detection and tracking in indoor environment using a static camera. The proposed method is based on object recognition in still images combined with methods using temporal information from the video. Doing that, we improve the performance of the overall system and reduce the task complexity. We first use background subtraction to limit the search space of the classifier. The segmentation is realized by modeling each background pixel by a single Gaussian model. As each connected component detected by the background subtraction potentially corresponds to one person, each blob is independently tracked. The tracking process is based on the analysis of connected components position and interest points tracking. In order to know the nature of various objects that could be present in the scene, we use multiple cascades of boosted classifiers based on Haar-like filters. We also present in this article a wide evaluation of this system based on a large set of videos.
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