Action recognition from a different view

In this paper, we propose a novel approach to recognise human activities from a different view. Although appearance-based recognition methods have been shown to be unsuitable for action recognition for varying views, there must be some regularity among the same action sequences of different views. Self-similarity matrices appear to be relative stable across views. However, the ability to effectively realise this stability is a problem. In this paper, we extract the shape-flow descriptor as the low-level feature and then choose the same number of key frames from the action sequences. Self-similarity matrices are obtained by computing the similarity between any pair of the key frames. The diagonal features of the similarity matrices are extracted as the highlevel feature representation of the action sequence and Support Vector Machines (SVM) is employed for classification. We test our approach on the IXMAS multi-view data set. The proposed approach is simple but effective when compared with other algorithms.