Human Interaction Recognition Using Independent Subspace Analysis Algorithm

Human interaction recognition has been widely studied because it has great scientific importance and many practical applications. Most existing methods rely on spatio-temporal local features (i.e. SIFT), human poses, and human joints to model human interactions. Motivated by the success of deep learning networks, we introduce a three-layer convolutional network which uses the Independent Subspace Analysis (ISA) algorithm to learn hierarchical invariant features from videos. The obtained invariant features are used as the inputs to a standard bag-of-features (BOF) model to recognize human interactions. We investigate the performance of our approach and the effectiveness of hierarchical invariant features on video sequences of the UT-Interaction dataset which contain both interacting persons and irrelevant pedestrians in the scenes. Experimental results show that our three-layer convolutional ISA network is able to learn features which are effective to represent complex activities such as human interactions in realistic environments.