A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 4 Issue 4
Oct.  2017

IEEE/CAA Journal of Automatica Sinica

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    CiteScore: 11.2, Top 5% (Q1)
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Xiaohui Yuan, Longbo Kong, Dengchao Feng and Zhenchun Wei, "Automatic Feature Point Detection and Tracking of Human Actions in Time-of-flight Videos," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 677-685, Oct. 2017. doi: 10.1109/JAS.2017.7510625
Citation: Xiaohui Yuan, Longbo Kong, Dengchao Feng and Zhenchun Wei, "Automatic Feature Point Detection and Tracking of Human Actions in Time-of-flight Videos," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 677-685, Oct. 2017. doi: 10.1109/JAS.2017.7510625

Automatic Feature Point Detection and Tracking of Human Actions in Time-of-flight Videos

doi: 10.1109/JAS.2017.7510625
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  • Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body image. Yet, occlusion and robustness are still open challenges. In this paper, we present an automatic, model-free feature point detection and action tracking method using a time-of-flight camera. Our method automatically detects feature points for movement abstraction. To overcome errors caused by miss-detection and occlusion, a refinement method is devised that uses the trajectory of the feature points to correct the erroneous detections. Experiments were conducted using videos acquired with a Microsoft Kinect camera and a publicly available video set and comparisons were conducted with the state-of-the-art methods. The results demonstrated that our proposed method delivered improved and reliable performance with an average accuracy in the range of 90%. The trajectorybased refinement also demonstrated satisfactory effectiveness that recovers the detection with a success rate of 93.7%. Our method processed a frame in an average time of 71.1 ms.

     

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