Discriminative spatio-temporal pattern discovery for 3D action recognition

Despite the recent success of 3D action recognition using depth sensor, most existing works target how to improve the action recognition performance, rather than understanding how different types of actions are performed. In this paper, we propose to discover discriminative spatio-temporal patterns for 3D action recognition. Discovering these patterns can not only help to improve the action recognition performance but also help us to understand and differentiate between the action category. Our proposed method takes the spatio-temporal structure of 3D action into consideration and can discover essential spatio-temporal patterns that play key roles in action recognition. Instead of relying on an end-to-end network to learn the 3D action representation and perform classification, we simply present each 3D action as a series of temporal stages composed by 3D poses. Then, we rely on nearest neighbor matching and bilinear classifiers to simultaneously identify both critical temporal stages and spatial joints for each action class. Despite using raw action representation and a linear classifier, experiments on five benchmark data sets show that the proposed spatio-temporal naïve Bayes mutual information maximization can achieve a competitive performance compared with the state-of-the-art methods that use sophisticated end-to-end learning, and has the advantage of finding discriminative spatio-temporal action patterns.