機械知覚&ロボティクスグループ
中部大学

People Image Analysis 学術論文(E)

A Single Framework for Action Recognition Based on Boosted Randomized Trees

Author
Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi
Publication
Information Processing Society of Japan Transactions on Computer Vision and Applications, vol. 3 pp. 160–171, 2011

Human detection and action recognition form the basis for understanding human behaviors. Human detection is used to detect the positions of humans, and action recognition is able to recognize the action of specific humans. However, numerous approaches have been used to handle action recognition and human detection separately. Therefore, three main issues still exist when independent methods of human detection and action recognition are combined, 1) intrinsic errors in object detection impact the performance of action recognition, 2) features common to action recognition and object detection are missed, 3) the combination also has an impact on processing speed. We propose a single framework for human detection and action recognition to solve these issues. It is based on a hierarchical structure called Boosted Randomized Trees. The nodes are trained such that the upper nodes detect humans from the background, while the lower nodes recognize action. We were able to improve both human detection and action recognition rates over earlier hierarchical structure approaches with the proposed method.

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