Statistical and entropy based multi purpose human motion analysis

As visual surveillance systems are gaining wider usage in a variety of fields, they need to be embedded with the capability to interpret scenes automatically, which is known as human motion analysis (HMA). However, existing HMA methods are too domain specific and computationally expensive. This pape...

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Bibliographic Details
Main Authors: Chin-Poo Lee, Kian-Ming Lim, Wei-Lee Woon
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Artificial neural networks
Computer vision
Entropy
Hidden Markov models
Image Processing
Motion Analysis
Motion segmentation
Neural Networks
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Summary:As visual surveillance systems are gaining wider usage in a variety of fields, they need to be embedded with the capability to interpret scenes automatically, which is known as human motion analysis (HMA). However, existing HMA methods are too domain specific and computationally expensive. This paper proposes a general purpose HMA method. It is based on the idea that human beings tend to exhibit random motion patterns during abnormal situations. Hence, angular and linear displacements of limb movements are characterized using basic statistical quantities. In addition, it is enhanced with the entropy of the Fourier spectrum to measure the randomness of the abnormal behavior. Various experiments have been conducted and prove that the proposed method has very high classification accuracy in identifying anomalous behavior.
DOI:10.1109/ICSPS.2010.5555261