A Spatiotemporal Robust Approach for Human Activity Recognition

Nowadays, human activity recognition is considered to be one of the fundamental topics in computer vision research areas, including human-robot interaction. In this work, a novel method is proposed utilizing the depth and optical flow motion information of human silhouettes from video for human activity recognition. The recognition method utilizes enhanced independent component analysis (EICA) on depth silhouettes, optical flow motion features, and hidden Markov models (HMMs) for recognition. The local features are extracted from the collection of the depth silhouettes exhibiting various human activities. Optical flow-based motion features are also extracted from the depth silhouette area and used in an augmented form to form the spatiotemporal features. Next, the augmented features are enhanced by generalized discriminant analysis (GDA) for better activity representation. These features are then fed into HMMs to model human activities and recognize them. The experimental results show the superiority of the proposed approach over the conventional ones.