Adaptive video object proposals by a context-aware model

Wenjing Geng, Chunlong Zhang, Gangshan Wu

Abstract: Most previous works focus on image object proposals while few on video object proposals. Besides, the existing explorations about video object proposals mainly concentrate on localizing the dominant object. In this paper, we aim at exploring a uniform framework for proposing multi-objects in videos no matter they are in the foreground or background. The method is derived from image object proposals, and makes best use of video characteristics. To achieve this task, we propose an adaptive context-aware model for video object proposals. First, spatial candidate windows are generated by the image method for acquiring the adequate bounding box samples. Temporal boxes are calculated by the motion based mapping. Considering the mapping loss, we define a box confidence coefficient contributing to keeping the proposal consistency and restraining the motion blur. The output proposal bounding boxes are ranked based on the scores calculated by the weighted scoring system. The proposed method is separately evaluated on the proposed multi-object dataset and the public dataset. The results compared with several state-of-the-arts show that our method has the most satisfactory overall performance for multi-object proposals in videos.