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This paper presents a hierarchal, two-layer, connectionist-based human-action recognition system (CHARS) as a first step towards developing socially intelligent robots. The first layer is a K-nearest neighbor (K-NN) classifier that categorizes human actions into two classes based on the existence of locomotion, and the second layer consists of two multi-layer recurrent neural networks that distinguish between subclasses within each class. A pyramid of histograms of oriented gradients (PHOG) descriptor is proposed for extracting local and spatial features. The PHOG descriptor reduces the dimensionality of input space drastically, which results in better convergence for the learning and classification processes. Computer simulations were conducted to illustrate the performance of the proposed CHARS and the role of temporal factor in solving this problem. A widely used KTH human-action database and the human-action dataset from our lab were utilized for performance evaluation. The proposed CHARS was found to perform better than other existing human-action recognition methods and achieved a 95.55% recognition rate.

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