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Unusual human behavior recognition using evolutionary technique
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DOI：10.1016/j.cie.2008.09.043.png)
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Abstract
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The vision for surveillance is an important task in many computer vision applications. The monitoring system concerns the tracking and recognition of people, and more generally, the understanding of human behaviors, from image sequences involving humans. Several methods for human tracking and human behavior recognition have been proposed by various researchers. But most of those do not have versatility and flexibility. In this paper, we propose an efficient and robust object tracking algorithm which use the color features, the distance features and count feature based oil all evolutionary techniques to measure the observation similarity. And then we will track each person and classify their behavior properties by analyzing their trajectory pattern. We propose multi-layer perceptron based oil hybrid genetic algorithm using Gaussian synapse make the recognition algorithm very efficient and robust for classify human behavior by trajectory pattern. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords:
People tracking
Unusual behavior
Evolutionary technique
Hybrid genetic algorithm
Gaussian synapse
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