A novel multispectral corner detector and a new local descriptor: an application to human posture recognition

Human posture recognition is an important task for intelligent systems specially those performing action recognition. In this paper, we propose a novel multispectral corner detector and a new HOG-based multispectral local descriptor. First, we select salient features which are extracted from an edge image obtained by picking the maximum eigenvalue of the jacobian matrix. Second, we extract for each feature point a local descriptor which combines both the Lab colour channels and depth information in a well-posed way using the Jacobian matrix. Last, we conduct a one-against-all learning strategy using both an incremental Covariance-guided One-Class Support Vector Machine (iCOSVM) and a Convolutional Neural Network (CNN). Experimental results show that we outperform the state-of-the-art methods whether our descriptor is combined with iCOSVM and with CNN.