Dr. Yanming Shao Profile

Dr. Yanming Shao

at Shanghai Aerospace Control Technology Institute

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Proceedings Article | 5 November 2020 Paper

Research on real time tracking algorithm of multispectral mosaic image based on the fast DSST algorithm

Yanming Shao, Dapeng Li, Xiaotong Zhang, Qianjin Lin, Huabao Long

Proceedings Volume 11567, 115673W (2020) https://doi.org/10.1117/12.2580259

KEYWORDS: Detection and tracking algorithms, Multispectral imaging, Target detection, Image filtering, Target recognition, Dimension reduction, Hyperspectral target detection, Cameras

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In order to solve the problem of traditional target recognition and tracking algorithms of the multispectral image such as high computation complexity, poor real time performance and low stability under complex scene and great variation of target appearance, a new mosaic image tracking algorithm based on dimension reduction of HOG feature data and multi-scale correlation filter is proposed in this paper. Firstly, in order to reduce the calculation complexity as well as to enhance the detection rate of small target, the 2D multispectral mosaic image data instead of the traditional 3D multispectral image data cubes is used, Then the histogram of oriented gradient (HOG) feature is extracted from the mosaic image data, and the singular value decomposition (SVD) algorithm with improved threshold selection method is adopted to reduce the dimension of the HOG feature matrix. Compared to the method which extracts HOG feature after dimension reduction, the proposed method takes advantage of high recognition precision, simple operation and high real-time performance. Finally, the target tracking is realized based on the dimension-reduced HOG feature with the fast discriminative scale space tracker (fDSST) algorithm which combines the scale filter and the position filter. A multispectral image dataset for target tracking was established, including different target occlusion, motion blur, variation of target scale and target appearance. Target tracking results on the dataset show the proposed algorithm can realize good tracking continuity and stability even if there exist different ground objects, variation in the appearance of the target shape, or target reappearance after occlusion.

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