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.

基于视觉嵌入的一种新的多目标跟踪方法，通过预测遮挡信息和利用遮挡线索和视觉嵌入生成两个独立的嵌入来解决在线跟踪场景中由于遮挡引起的跟踪中断问题，实验结果表明在有遮挡和无遮挡的跟踪场景中具有良好的性能水平。