Towards wide-scale continuous gesture recognition model for in-depth and grayscale input videos

摘要

In recent years, gesture recognition in video sequences has aroused growing interest in the fields of computer vision and behavioral understanding, for example in the control of robots and video games, in the field of video surveillance, automatic video indexing or content-based video retrieval. Processing large-scale continuous gesture data with in-depth, grayscale input videos remains a primary challenge for academic researchers. A wide range of recognition models have been proposed to solve this problem but have not proven their great performance. The main contribution of this article to address this problem is to segment the sequences of continuous gestures into isolated gestures, using the average of the velocity information calculated on the basis of the estimate of the deep optical flow, and to extract a set of relevant descriptors, called characteristics. signature, in order to characterize different intensities and spatial information describing the location, speed and orientation of movement. Finally, to transmit to a linear SVM the characteristics built for the depth and gray scale sequences, for each isolated segment for its classification. The experimental study carried out on the various standard data collections namely KTH, Chalearn and Weizmann, on our model and on the main models that we have studied in the literature, as well as the analysis of the results, which we obtained, clearly show the limits of these studied models and confirms the performance of our model as well as efficiency in terms of precision, recall and robustness.