Multi-Pedestrian Tracking with Clusters

One of the biggest challenges in multi-pedestrian tracking arises in crowds, where missing detections can lead to wrong track-detection assignments, especially under heavy occlusion. In order to identify such situations, we cluster tracks and detections based on their overlaps and introduce different cluster states depending on the number of detections and tracks in a cluster. On the basis of this strategy, we make the following contributions. First, we propose a cluster-aware non-maximum suppression (CA-NMS) that leverages temporal information from tracks applying an increased IoU threshold in clusters with severe occlusion to reduce the number of missed detections, while at the same time limiting the number of duplicate detections. Second, for clusters with very high overlaps where detections are missing even with the CA-NMS, we utilize past track information to correct wrong assignments when missed targets are re-detected after occlusion. Furthermore, we propose a new tracking pipeline that combines the paradigms of tracking-by-detection and regression-based tracking to improve the association performance in crowded scenes. Putting all together, our tracker achieves competitive results w.r.t. the state-of-the-art on three multi-pedestrian tracking benchmarks. Our framework is analyzed with extensive ablative experiments and the impact of the proposed tracking components on the performance is evaluated.