Task-Specific Spatiotemporal Context-Aware Decoupling for Occluded Video Object Detection

In complex visual scenarios, people can understand occluded objects through contextual reasoning and association. However, existing video object detection (VOD) methods could not work well. They treat all frames equally during feature aggregation and ignore the interdependence of objects between adjacent frames. This results in excessive noise during long-term feature alignment. To address these issues, we propose a task-specific spatiotemporal context-aware decoupling (TSCD) network, which adapts the global semantic information and local localization information needed for classification and regression tasks, respectively. Furthermore, a spatiotemporal context-aware feature matching (CAFM) module is utilized to refine the regression features while avoiding inter-frame feature mixing. Effective object associations are used to improve the regression of occluded object locations. Extensive experimental results on the ImageNet VID dataset and the OVIS dataset show that our proposed TSCD has significant advantages in occlusion scenes. The code is available at https://github.com/KaihongLi/TSCD.