Current state-of-the-art action detection systems are tailored for offline batch-processing applications. However, for online applications like human-robot interaction, current systems fall short, either because they only detect one action per video, or because they assume that the entire video is available ahead of time. In this work, we introduce a real-time and online joint-labelling and association algorithm for action detection that can incrementally construct space-time action tubes on the most challenging action videos in which different action categories occur concurrently. In contrast to previous methods, we solve the detection-window association and action labelling problems jointly in a single pass. We demonstrate superior online association accuracy and speed (2.2ms per frame) as compared to the current state-of-the-art offline systems. We further demonstrate that the entire action detection pipeline can easily be made to work effectively in real-time using our action tube construction algorithm.

本文介绍了一种实时、在线的联合标注和关联算法，用于动作检测，可以增量构建空间-时间动作管，在最具挑战性的动作视频中，可以同时发生不同的动作类别，其实时关联准确度和速度优于当前最先进的离线系统。

基于增量策略的人体动作检测管线构建