A light-weight on-line action detection with hand trajectories for industrial surveillance

Most of the intelligent surveillances in the industry only care about the safety of the workers. It is meaningful if the camera can know what, where and how the worker has performed the action in real time. In this paper, we propose a light-weight and robust algorithm to meet these requirements. By only two hands' trajectories, our algorithm requires no Graphic Processing Unit (GPU) acceleration, which can be used in low-cost devices. In the training stage, in order to find potential topological structures of the training trajectories, spectral clustering with eigengap heuristic is applied to cluster trajectory points. A gradient descent based algorithm is proposed to find the topological structures, which reflects main representations for each cluster. In the fine-tuning stage, a topological optimization algorithm is proposed to fine-tune the parameters of topological structures in all training data. Finally, our method not only performs more robustly compared to some popular offline action detection methods, but also obtains better detection accuracy in an extended action sequence.