Research on real – time tracking of table tennis ball based on machine learning with low-speed camera
Open Access
- 1 January 2018
- journal article
- research article
- Published by Taylor & Francis Ltd in Systems Science & Control Engineering
- Vol. 6 (1), 71-79
- https://doi.org/10.1080/21642583.2018.1450167
Abstract
This paper proposes a novel method to track table tennis ball in real time by a low-speed camera instead of a high-speed one. Several difficult problems are solved for practical applications, such as environmental interference, smear in low-speed video images and slow processing speed. In view of these difficulties, the VOCUS system is used to segment images and mark the three significant regions based on three contrast colour channels. These regions are utilized for image matching using the LGP+adaboost algorithm. As a strong classifier based on machine learning, adaboost algorithm can recognize the features of smear balls with different shapes. Therefore, the region that is most similar to smear ball from the three significant regions is regarded as a target. Afterwards, through the moving ROI area algorithm, the identification time is greatly shortened in real-time video tracking. Finally, the feasibility of the algorithm is examined by experiments.Funding Information
- Shanghai University of Sport (stfx0170115)
- Science and Technology Commission of Shanghai Municipality (15490503100)
This publication has 11 references indexed in Scilit:
- Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative StudyIEEE Transactions on Image Processing, 2012
- Center-surround divergence of feature statistics for salient object detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- A tracking and predicting scheme for ping pong robotJournal of Zhejiang University SCIENCE C, 2011
- Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware componentsJournal of Real-Time Image Processing, 2010
- Saliency, attention, and visual search: An information theoretic approachJournal of Vision, 2009
- Occlusion-Aware Optical Flow EstimationIEEE Transactions on Image Processing, 2008
- A model of saliency-based visual attention for rapid scene analysisIEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
- A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of informationJournal of Neuroscience, 1993
- “Determining optical flow”: a retrospectiveArtificial Intelligence, 1993
- An opponent-process theory of color vision.Psychological Review, 1957