Tracking Assisted Faster Video Object Detection

Recent approaches have achieved great success on still image object detection. Despite the high accuracy, directly applying image object detectors for video object detection is rather slow. Inspired from the fact that object tracking is much more efficient than object detection, we propose to combine object detection and tracking for fast video object detection. Computational expensive detection network is applied on sparsely arranged key frames, while proposals of non-key frames are obtained through tracking and regression of previous frame's proposals. Assisted with an adaptive key-frame arrangement module, our method can adaptively decide whether to track or to detect based on tracking quality. Extensive experiments show that the proposed method can significantly boost detection speed with a rather small drop in detection accuracy.