Improvement of HMM-based action classification by using state transition probability

Yuka Kitamura; Haruki Aruga; Manabu Hashimoto

doi:10.1117/12.2182836

30 April 2015 Improvement of HMM-based action classification by using state transition probability

Yuka Kitamura, Haruki Aruga, Manabu Hashimoto

Proceedings Volume 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015; 95340S (2015) https://doi.org/10.1117/12.2182836
Event: The International Conference on Quality Control by Artificial Vision 2015, 2015, Le Creusot, France

Abstract

We propose a method to classify multiple similar actions which are contained in human behaviors by considering a weak-constrained order of “actions”. The proposed method regards the human behavior as a combination of “action” patterns which have order constrained weakly. In this method, actions are classified by using not only image features but also consistency of transitions between an action and next action. By considering such an action transition, our method can recognize human behavior even if image features of different action are similar to each other. Based on this idea, we have improved the previous HMM-based algorithm effectively. Through some experiments using test image sequences of human behavior appeared in a bathroom, we have confirmed that the average classification success rate is 97 %, which is about 53 % higher than the previous method.

Citation Download Citation

Yuka Kitamura, Haruki Aruga, and Manabu Hashimoto "Improvement of HMM-based action classification by using state transition probability", Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 95340S (30 April 2015); https://doi.org/10.1117/12.2182836

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KEYWORDS

Image segmentation

Sensors

Data modeling

Image classification

Feature extraction

Image processing

Image sensors

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