Reliable Human Detection and Tracking in Top-View Depth Images

The paper presents a method for human detection and tracking in depth images captured by a top-view camera system. We introduce a new feature descriptor which outperforms state-of-the-art features like Simplified Local Ternary Patterns in the given scenario. We use this feature descriptor to train a head-shoulder detector using a discriminative class scheme. A separate processing step ensures that only a minimal but sufficient number of head-shoulder candidates is evaluated. This contributes to an excellent runtime performance. A final tracking step reliably propagates detections in time and provides stable tracking results. The quality of the presented method allows us to recognize many challenging situations with humans tailgating and piggybacking.