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Proceedings Paper

Human activities recognition by head movement using partial recurrent neural network
Author(s): Henry C. C. Tan; Kui Jia; Liyanage C. De Silva
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Paper 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.

Paper Details

Date Published: 23 June 2003
PDF: 8 pages
Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); doi: 10.1117/12.503257
Show Author Affiliations
Henry C. C. Tan, National Univ. of Singapore (Singapore)
Kui Jia, National Univ. of Singapore (Singapore)
Liyanage C. De Silva, National Univ. of Singapore (Singapore)


Published in SPIE Proceedings Vol. 5150:
Visual Communications and Image Processing 2003
Touradj Ebrahimi; Thomas Sikora, Editor(s)

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