Adaptive block online learning target tracking based on super pixel segmentation

Yue Cheng; Jianzeng Li II

doi:10.1117/12.2302930

10 April 2018 Adaptive block online learning target tracking based on super pixel segmentation

Yue Cheng, Jianzeng Li II

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061526 (2018) https://doi.org/10.1117/12.2302930
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

Video target tracking technology under the unremitting exploration of predecessors has made big progress, but there are still lots of problems not solved. This paper proposed a new algorithm of target tracking based on image segmentation technology. Firstly we divide the selected region using simple linear iterative clustering (SLIC) algorithm, after that, we block the area with the improved density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm. Each sub-block independently trained classifier and tracked, then the algorithm ignore the failed tracking sub-block while reintegrate the rest of the sub-blocks into tracking box to complete the target tracking. The experimental results show that our algorithm can work effectively under occlusion interference, rotation change, scale change and many other problems in target tracking compared with the current mainstream algorithms.

Citation Download Citation

Yue Cheng and Jianzeng Li II "Adaptive block online learning target tracking based on super pixel segmentation", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061526 (10 April 2018); https://doi.org/10.1117/12.2302930

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