Multiple Object Tracking based on Occlusion-Aware Embedding Consistency
  Learning
Yaoqi Hu Axi Niu Yu Zhu Qingsen Yan Jinqiu Sun Yanning Zhang
The Joint Detection and Embedding (JDE) framework has achieved remarkable
progress for multiple object tracking. Existing methods often employ extracted
embeddings to re-establish associations between new detections and previously
disrupted tracks. However, the reliability of embeddings diminishes when the
region of the occluded object frequently contains adjacent objects or clutters,
especially in scenarios with severe occlusion. To alleviate this problem, we
propose a novel multiple object tracking method based on visual embedding
consistency, mainly including: 1) Occlusion Prediction Module (OPM) and 2)
Occlusion-Aware Association Module (OAAM). The OPM predicts occlusion
information for each true detection, facilitating the selection of valid
samples for consistency learning of the track's visual embedding. The OAAM
leverages occlusion cues and visual embeddings to generate two separate
embeddings for each track, guaranteeing consistency in both unoccluded and
occluded detections. By integrating these two modules, our method is capable of
addressing track interruptions caused by occlusion in online tracking
scenarios. Extensive experimental results demonstrate that our approach
achieves promising performance levels in both unoccluded and occluded tracking
scenarios.