PFET: Multi-Vehicle Tracking with Pseudo-Feature Embeddings for Traffic Video Surveillance
Qing-you He, Liangqun Li · 2022 25th International Conference on Information Fusion (FUSION) · 2022
Advances in multi-object tracking (MOT) provide possibilities for unmanned monitoring across intelligent transportation. The tracking-by-detection paradigm aims at associating detection boxes whose scores are higher than a threshold with active trajectories. However, when there is no valid detection associated with certain trajectories due to occlusions or false negatives (FN) may leads to fragmented trajectories and ID switches. An effective trajectory recovery method is proposed for multi-vehicle tracking in traffic surveillance in this paper. The proposed method provides a set of pseudo-feature embeddings to fill the gaps in the fragmented trajectories. These embeddings with motion and appearance features can be re-associated with new detections through a cascaded matching strategy that improves the quality of data association. Experimental results prove that our method can effectively reduce the number of ID switches and fragmentations. It is worth noting that the proposed method without a deep model achieves high tracking speed with considerable accuracy compared to state-of-the-art methods.