Online multiple object tracking using joint detection and embedding network

Highlights：

• The Path Aggregation Network was harnessed to combine the lowresolution and the high-resolution information for intergrating texture features and semantic information to mitigate alignment of the Re-ID features.

• The BNNeck is designed to avail the network for learning the best embeddings. The effectiveness of the two-stage progressive learning approach (BNNeck) is discussed and analyzed for ID-discriminative embedding of joinly trained detection and tracking. It effectively reduces the number of ID switches in MOT. The proposed tracker outperforms other state-of-the-art MOT trackers in terms of accuracy and efficiency on the three publicly challenging datasets of MOT15, MOT16 and MOT17.

摘要

•A novel architecture was proposed called YOLOTracker that performs online MOT by exploiting joint detection and embedding network, which shows the powerful and efficient performance in MOT benchmarks.•The Path Aggregation Network was harnessed to combine the lowresolution and the high-resolution information for intergrating texture features and semantic information to mitigate alignment of the Re-ID features.•The BNNeck is designed to avail the network for learning the best embeddings. The effectiveness of the two-stage progressive learning approach (BNNeck) is discussed and analyzed for ID-discriminative embedding of joinly trained detection and tracking. It effectively reduces the number of ID switches in MOT. The proposed tracker outperforms other state-of-the-art MOT trackers in terms of accuracy and efficiency on the three publicly challenging datasets of MOT15, MOT16 and MOT17.