Human action recognition framework by fusing multiple features

In this paper, we propose a framework which fuses multiple features for action recognition in depth sequence. The fusion of multiple features is important for recognizing action since a single feature-based representation is inadequate to capture the variants. Hence, we use two types of features: i) a quantized vocabulary of local spatio-temporal descriptor HOG3D, and ii) a global projection based descriptor that computes the HOG from the Depth Motion Maps. To optimally combine these features, we input those features to different classifiers, where SVM is applied to estimate the probabilities of action labels. Then, we weight those probabilities respectively and sum it to find the maximum score of action labels. The proposed approach is tested on publicly available MSR Action3D dataset which demonstrates that fusion of multiple features help to achieve improved performance significantly, outperforming Li et al.[1] in most of the cases.