Clustered multi-task linear discriminant analysis for view invariant color-depth action recognition

The widespread adoption of low-cost depth cameras has opened new opportunities to improve traditional action recognition systems. In this paper we focus on the specific problem of action recognition under view point changes and propose a novel approach for view-invariant action recognition operating jointly on visual data of color and depth camera channels. Our method is based on the unique combination of robust Self-Similarity Matrix (SSM) descriptors and multi-task learning. Indeed, multi-view action recognition is inherently a multi-task learning problem: images from a camera view can be modeled as visual data associated to the same task and it is reasonable to assume that the data of different tasks (camera views) are related to each other. In this work we propose a novel algorithm extending Multi-Task Linear Discriminant Analysis (MT-LDA) to enhance its flexibility by learning the dependencies between different views. Extensive experimental results on the publicly available ACT42 dataset demonstrate the effectiveness of the proposed method.