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Your search keyword '"Liang, Qiaokang"' showing total 84 results
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84 results on '"Liang, Qiaokang"'

60. Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm.

Author

Liang, Qiaokang, Wu, Wanneng, Yang, Yukun, Zhang, Ruiheng, Peng, Yu, and Xu, Min

Subjects
SPORTS films, TRACKING algorithms, COMPUTER vision, INTEGERS, OBJECT tracking (Computer vision), ALGORITHMS
Abstract

Sports analysis has recently attracted increasing research efforts in computer vision. Among them, basketball video analysis is very challenging due to severe occlusions and fast motions. As a typical tracking-by-detection method, k-shortest paths (KSP) tracking framework has been well used for multiple-person tracking. While effective and fast, the neglect of the appearance model would easily lead to identity switches, especially when two or more players are intertwined with each other. This paper addresses this problem by taking the appearance features into account based on the KSP framework. Furthermore, we also introduce a similarity measurement method that can fuse multiple appearance features together. In this paper, we select jersey color and jersey number as two example features. Experiments indicate that about 70% of jersey color and 50% of jersey number over a whole sequence would ensure our proposed method preserve the player identity better than the existing KSP tracking method. [ABSTRACT FROM AUTHOR]

Published
2020
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84. Defect detection in code characters with complex backgrounds based on BBE.

Author

Peng J, Zhu W, Liang Q, Li Z, Lu M, Sun W, and Wang Y

Subjects
Image Processing, Computer-Assisted, Algorithms, Neural Networks, Computer
Abstract

Computer vision technologies have been widely implemented in the defect detection. However, most of the existing detection methods generally require images with high quality, and they can only process code characters on simple backgrounds with high contrast. In this paper, a defect detection approach based on deep learning has been proposed to efficiently perform defect detection of code characters on complex backgrounds with a high accuracy. Specifically, image processing algorithms and data enhancement techniques were utilized to generate a large number of defect samples to construct a large data set featuring a balanced positive and negative sample ratio. The object detection network called BBE was build based on the core module of EfficientNet. Experimental results show that the mAP of the model and the accuracy reach 0.9961 and 0.9985, respectively. Individual character detection results were screened by setting relevant quality inspection standards to evaluate the overall quality of the code characters, the results of which have verified the effectiveness of the proposed method for industrial production. Its accuracy and speed are high with high robustness and transferability to other similar defect detection tasks. To the best of our knowledge, this report describes the first time that the BBE has been applied to defect inspections for real plastic container industry.

Published
2021
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