Simultaneous segmentation and classification of human actions in video streams using deeply optimized Hough transform

Most researches on human activity recognition do not take into account the temporal localization of actions. In this paper, a new method is designed to model both actions and their temporal domains. This method is based on a new Hough method which outperforms previous published ones on honeybee dataset thanks to a deeper optimization of the Hough variables. Experiments are performed to select skeleton features adapted to this method and relevant to capture human actions. With these features, our pipeline improves state-of-the-art performances on TUM dataset and outperforms baselines on several public datasets. •We offer a learning process for Hough transform.•This method outperforms other Hough method on honeybee dataset.•We apply this new method on human action segmentation.•We evaluate the pipeline on TUM and UTKAD datasets.•Our results are superior to the best published ones.