Human activities recognition by head movement using partial recurrent neural network

Henry C. C. Tan; Kui Jia; Liyanage C. De Silva

doi:10.1117/12.503257

23 June 2003 Human activities recognition by head movement using partial recurrent neural network

Henry C. C. Tan, Kui Jia, Liyanage C. De Silva

Proceedings Volume 5150, Visual Communications and Image Processing 2003; (2003) https://doi.org/10.1117/12.503257
Event: Visual Communications and Image Processing 2003, 2003, Lugano, Switzerland

Abstract

Traditionally, human activities recognition has been achieved mainly by the statistical pattern recognition methods or the Hidden Markov Model (HMM). In this paper, we propose a novel use of the connectionist approach for the recognition of ten simple human activities: walking, sitting down, getting up, squatting down and standing up, in both lateral and frontal views, in an office environment. By means of tracking the head movement of the subjects over consecutive frames from a database of different color image sequences, and incorporating the Elman model of the partial recurrent neural network (RNN) that learns the sequential patterns of relative change of the head location in the images, the proposed system is able to robustly classify all the ten activities performed by unseen subjects from both sexes, of different race and physique, with a recognition rate as high as 92.5%. This demonstrates the potential of employing partial RNN to recognize complex activities in the increasingly popular human-activities-based applications.

Citation Download Citation

Henry C. C. Tan, Kui Jia, and Liyanage C. De Silva "Human activities recognition by head movement using partial recurrent neural network", Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); https://doi.org/10.1117/12.503257

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