Online Video Object Detection Using Association LSTM

Yongyi Lu, Cewu Lu, Chi-Keung Tang; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2344-2352

Video object detection is a fundamental tool for many applications. Since direct application of image-based object detection cannot leverage the rich temporal information inherent in video data, we advocate to the detection of long-range video object pattern. While the Long Short-Term Memory (LSTM) has been the de facto choice for such detection, currently LSTM cannot fundamentally model object association between consecutive frames. In this paper, we propose the association LSTM to address this fundamental association problem. Association LSTM not only regresses and classifiy directly on object locations and categories but also associates features to represent each output object. By minimizing the matching error between these features, we learn how to associate objects in two consecutive frames. Additionally, our method works in an online manner, which is important for most video tasks. Compared to the traditional video object detection methods, our approach outperforms them on standard video datasets.