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Improving human activity detection by combining multi-dimensional motion descriptors with boosting

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4 Author(s)
Ogata, T. ; Dept. of Mech. & Control Eng., Kyushu Inst. of Technol., Fukuoka ; Christmas, W. ; Kittler, J. ; Ishikawa, S.

A new, combined human activity detection method is proposed. Our method is based on Efros et al.'s motion descriptors (2003) and Ke et al.'s event detectors (2005). Since both methods use optical flow, it is easy to combine them. However, the computational cost of the training increases considerably because of the increased number of weak classifiers. We reduce this computational cost by extending Ke et al.'s weak classifiers to incorporate multi-dimensional features. The proposed method is applied to off-air tennis video data, and its performance is evaluated by comparison with the original two methods. Experimental results show that the performance of the proposed method is a good compromise in terms of detection rate and of computation time of testing and training

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Pattern Recognition, 2006. ICPR 2006. 18th International Conference on  (Volume:1 )

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