Robust information fusion in the DOHT paradigm for real-time action detection

In the increasingly explored domain of action analysis, our work focuses on action detection-i.e., segmentation and classification-in the context of real applications. Hough transform paradigm fits well for such applications. In this paper, we extend deeply optimized Hough transform paradigm to handle various feature types and to merge information provided by multiple sensors-e.g., RBG sensors, depth sensors and skeleton data. To this end, we propose and compare three fusion methods applied at different levels of the algorithm, one being robust to data losses and, thus, to sensor failure. We deeply study the influence of merged features on the algorithm's accuracy. Finally, since we consider real-time applications such as human interactions, we investigate the latency and computation time of our proposed method.