Abstract
In this paper, we propose an automated habit detection system. We define a “habit” in this study as some motion that is significantly different from our common behaviors. The behaviors of two subjects during conversation are tracked by the Kinect sensor and their skeletal and facial conformations are detected. The proposed system detects the motions considered as habits by analyzing them using a principal component analysis (PCA) and wavelet multi-resolution analysis (MRA). In our experiments, we prepare a total of 108 movies containing 5 min of conversation. Of these, 100 movies are used to build the average motion model (AMM), and the remainder are used for the evaluation. The accuracy of habit detection in the proposed system is shown to have a precision of 84.0 \(\%\) and a recall of 81.8 \(\%\).
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© 2015 Springer International Publishing Switzerland
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Misawa, H., Obara, T., Iyatomi, H. (2015). Automated Habit Detection System: A Feasibility Study. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_2
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DOI: https://doi.org/10.1007/978-3-319-27863-6_2
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