Close-Range Human Detection for Head-Mounted Cameras

Dennis Mitzel and Bastian Leibe

Paper

Abstract

In this paper we consider the problem of multi-person detection and tracking from the perspective of a head mounted stereo camera. As pedestrians close to the camera cannot be detected by classical full-body detectors due to strong occlusion, we propose a stereo depth-template based detection approach for close-range pedestrians. We per- form a classical sliding window procedure, where we measure the similarity between a learned depth template and the depth image. To reduce the search space of the detector we slide the detector only over few selected regions of interest that are generated based on the depth information. The region of interest selection allows us to further constrain the number of scales to be evaluated, significantly reducing the computational cost. We present experiments on stereo sequences recorded from a head-mounted camera setup in crowded shopping street scenarios and show that our proposed approach achieves supe- rior performance on this very challenging data.


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