Abstract: Video object detection(VID) aims to exploit temporal information between video frames to assist object detection.Unlike still-image detection,VID can leverage rich temporal context by referencing features from other frames to enhance the detection performance.Therefore,existing methods generally focus on aggregating features across multiple frames to construct temporally enhanced feature representations and improve detection accuracy.However,in video sequences,objects often undergo appearance degradation due to factors such as occlusion and motion blur,making it difficult for single-frame detectors to generate high-quality candidate regions.This,in turn,can cause the subsequent cross-frame feature aggregation to fail.To address this issue,this paper proposes a tracking-assisted video object detection method designed to mitigate aggregation failures caused by missing candidate regions.Specifically,it introduces a lightweight tracker and designs a feature selection module to jointly utilize detection outputs and motion priors provided by the tracker.It then employs the WBF(Weighted Box Fusion) algorithm to merge detection boxes with tracking predictions,producing more reliable candidate regions under challenging conditions such as occlusion.Finally,it extracts these high-quality candidate regions and their corresponding trajectory features for subsequent temporal aggregation.Experiments on the ImageNet VID dataset demonstrate that the proposed method effectively enhances robustness in occluded scenarios and achieves higher detection accuracy compared with previous VID approaches.

Key words: Video object detection, Object detection, Object tracking, Feature aggregation, Weighted box fusion