Dr. Chao Tang Profile

Dr. Chao Tang

at Hefei Univ

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Proceedings Article | 31 August 2018 Paper

Human action recognition using Kinect multimodal information

Chao Tang, Miao-hui Zhang, Xiao-feng Wang , Wei Li, Feng Cao, Chun-ling Hu

Proceedings Volume 10835, 1083507 (2018) https://doi.org/10.1117/12.2505416

KEYWORDS: 3D image processing, Feature extraction, Detection and tracking algorithms

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With the successful introduction and popularization of Kinect, it has been widely applied in intelligent surveillance, human-machine interaction and human action recognition and so on. This paper presents a human action recognition based on multimodal information using the Kinect sensor. Firstly, the HOG feature based on RGB modal information, the space-time interest points feature based on depth modal information, and the human body joints relative position feature based on skeleton modal information are extracted respectively for expressing human action. Then, the three kinds of nearest neighbor classifiers with different distance measurement formulas are used to predict the class label for a test sample which is respectively expressed by three different modal features. The experimental results show that the proposed method is simple, fast and efficient compared with other action recognition algorithms on public datasets.

Proceedings Article | 15 November 2017 Paper

Robotic fish tracking method based on suboptimal interval Kalman filter

Xiaohong Tong, Chao Tang

Proceedings Volume 10605, 106051J (2017) https://doi.org/10.1117/12.2288914

KEYWORDS: Filtering (signal processing), Robotics, Monte Carlo methods

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Autonomous Underwater Vehicle (AUV) research focused on tracking and positioning, precise guidance and return to dock and other fields. The robotic fish of AUV has become a hot application in intelligent education, civil and military etc. In nonlinear tracking analysis of robotic fish, which was found that the interval Kalman filter algorithm contains all possible filter results, but the range is wide, relatively conservative, and the interval data vector is uncertain before implementation. This paper proposes a ptimization algorithm of suboptimal interval Kalman filter. Suboptimal interval Kalman filter scheme used the interval inverse matrix with its worst inverse instead, is more approximate nonlinear state equation and measurement equation than the standard interval Kalman filter, increases the accuracy of the nominal dynamic system model, improves the speed and precision of tracking system. Monte-Carlo simulation results show that the optimal trajectory of sub optimal interval Kalman filter algorithm is better than that of the interval Kalman filter method and the standard method of the filter.

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