Context-Aware Video Object Proposals

Recent advances in object proposals have been achieved obvious performance to speed up sliding window based object detection or recognition. However, the spatial-temporal object proposal of multi-objects in video is still a challenging problem. Applying the existing image methods frame by frame will result in three defects. First, no guarantee to keep the consistent proposal results, i.e., it is hard to avoid omitting objects even in consecutive or similar sequences. Second, the latent information contained in time dimension would not be made best use of to improve the detection rate. Third, due to the motion blur caused by motion flow, the efficiency of object proposals relying on contour or edge features would be definitely degraded. In this paper, we propose an efficient method for video object proposals. By introducing image method into context-aware framework, we get the improved detection rate compared to the frame by frame usage, while keeping a controllable computing efficiency. Firstly, the bounding boxes produced by image proposals are used as the input. Then the candidate windows are scored with contextual information by generating motion-based mapping boxes. To evaluate the multi-object proposal results, we build a specific dataset. Experiments show that the proposed method can improve the detection rate of the original image method, and especially achieve better performance when proposing a small set of bounding boxes.