Information Fusion for Human Action Recognition via Biset/Multiset Globality Locality Preserving Canonical Correlation Analysis

In this paper, we study the problem of human action recognition, in which each action is captured by multiple sensors and represented by multisets. We propose two novel information fusion techniques for fusing the information from multisets. The first technique is biset globality locality preserving canonical correlation analysis (BGLPCCA), which aims to learn the common feature subspace between two sets. The second technique is multiset globality locality preserving canonical correlation analysis (MGLPCCA), which aims to deal with three or more sets. The proposed BGLPCCA and MGLPCCA are able to learn a low-dimensional common subspace that preserves the local and global structures of data samples. Moreover, two novel descriptors are presented for both depth and skeleton. We then propose a new human action recognition framework employing the proposed BGLPCCA or MGLPCCA to learn the shared subspace from multiple sets of features including skeleton, depth, and optical flow. Extensive experiments on five publicly available datasets (MSR Action3D, UTD multimodal human action dataset, multimodal action database, Kinect activity recognition dataset, and SBU Kinect interaction dataset) demonstrate the effectiveness of the proposed framework.