Multi-task linear discriminant analysis for multi-view action recognition
Action recognition is a central problem in many practical applications, such as video annotation, video surveillance and human-computer interaction. Most action recognition approaches are currently based on localized spatio-temporal features that can vary significantly when the viewpoint changes. Therefore, the performance rapidly drops when training and test data correspond to different cameras/viewpoints. Recently, Self-Similarity Matrix (SSM) features have been introduced to circumvent this problem. To improve the performance of current SSM-based methods, in this paper we propose a multi-task learning framework for multi-view action recognition where discriminative SSM features are shared among different views. Inspired by the mathematical connection between multivariate linear regression and Linear Discriminant Analysis (LDA), we propose a novel learning algorithm, where a single optimization framework is defined for multi-task multi-class LDA by choosing an appropriate class indicator matrix. Experimental results on the popular IXMAS dataset demonstrate that our approach achieves accurate performance and compares favorably with state-of-the-art methods.