Incremental discriminant-analysis of canonical correlations for action recognition
Human action recognition from video sequences is a challenging problem due to the large changes of human appearance in the cases of partial occlusions, non-rigid deformations, and high irregularities. It is difficult to collect a large set of training samples to learn the discriminative model with covering all possible variations of an action. In this paper, we propose an online recognition method, namely incremental discriminant-analysis of canonical correlations (IDCC), in which the discriminative model is incrementally updated to capture the changes of human appearance, and thereby facilitates the recognition task in changing environments. As the training sets are acquired sequentially instead of being given completely in advance, our method is able to compute a new discriminant matrix by updating the existing one using the eigenspace merging algorithm. Furthermore, we integrate our method into the graph-based semi-supervised learning method, linear neighbor propagation, to deal with the limited labeled training data. Experimental results on both Weizmann and KTH action data sets show that our method performs better than state-of-the-art methods on accuracy and efficiency.
incremental discriminant-analysis human action recognition computer vision