Abstract
In this chapter, a compact human action recognition system is presented with a view to applications in security systems, human-computer interaction, and intelligent environments. There are three main contributions: Firstly, the framework of an embedded human action recognition system based on a support vector machine (SVM) classifier and some compact motion features has been presented. Secondly, the limitations of the well-known motion history image (MHI) are addressed and a new motion history histograms (MHH) feature is introduced to represent the motion information in the video. MHH not only provides rich motion information, but also remains computationally inexpensive. We combine MHI and MHH into a low-dimensional feature vector for the system and achieve improved performance in human action recognition over comparable methods that use tracking-free temporal template motion representations. Finally, a simple system based on SVM and MHI has been implemented on a reconfigurable embedded computer vision architecture for real-time gesture recognition.
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Meng, H., Pears, N., Freeman, M., Bailey, C. (2009). Motion History Histograms for Human Action Recognition. In: Kisačanin, B., Bhattacharyya, S.S., Chai, S. (eds) Embedded Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84800-304-0_7
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DOI: https://doi.org/10.1007/978-1-84800-304-0_7
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