Dualfeat: Dual Feature Aggregation for Video Object Detection

Abstract

Video object detection aims to detect and track each object in a given video. However, due to the problem of appearance deterioration in the video, it is still challenging to obtain good results when we apply traditional image object detection methods to videos. In this paper, we propose a new feature aggregation method, called Dual Feature Aggregation (DualFeat) for video object detection. By effectively combining the temporal and spatial attention mechanisms, we make full use of the temporal and spatial information in videos. Meanwhile, we leverage a real-time tracker to track detected objects in video frames, where features are aggregated again with previously obtained features. Such a way helps to obtain more comprehensive and richer features, greatly improving the accuracy of video object detection. We perform experiments on the ILSVRC2017 dataset, and the experimental results also verify the effectiveness of our method.