On-demand Action Detection System using Pose Information

Human action detection is a very important yet difficult task for various multimedia applications such as safety surveillance, sports video analysis and video editing in media industry. Most existing methods proposed for action detection are machine learning based approaches, however, highly time- and cost-consuming to prepare training data with annotations. Thus, it is still very difficult to apply these methods for industrial applications where the actions of interests might happen rarely in real scenarios such as criminal or suspicious behaviors, because it is impossible to collect a large number of such training data for target actions. In this paper, we disruptively abandon these conventional methods, alternatively, adopting an on-demand retrieval approach using pose information to handle the action detection task. We introduce a demo system that can detect similar actions immediately by specifying a few second sample video without any training process. The system demonstrates the usability and efficacy of our on-demand approach for human action detection. The experimental results are reported to show that our approach outperforms the state-of-the-art method in higher precision and recall, up to 11% and 6.1% improvement, respectively.