Research on lamp auto-control system based on infrared image processing

Zhang, Zhifeng; Yusheng, Zhai; Su, Yanyan; Wang, Xinjie; Qiao, Lin; Liu, Hai-Zeng; Li, Shihai; Du, Yunchen

doi:10.1016/j.ijleo.2015.10.064

Cited by 4 publications

(1 citation statement)

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“…Visual object tracking, the problem of locating objects in a video sequence, is one of the central research topics in the field of computer vision [1] and Big Data Analytics (BDA). Recently more and more modern applications, such as precision guidance [2], intelligent surveillance [3], and auto-control system [4], etc., require object tracking result of high accuracy. According to the target changes in the process of tracking, object tracking can be divided into long-term tracking and short-term tracking.…”

Section: Introductionmentioning

confidence: 99%

Long-term real time object tracking based on multi-scale local correlation filtering and global re-detection

et al. 2020

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This paper investigates long-term visual object tracking which is a complex problem in computer vision community and big data analysis, due to the variation of the target and the surrounding environment. A novel tracking algorithm based on local correlation filtering and global keypoint matching is proposed to solve problems occurred during long-term tracking such as occlusion, target-losing, etc. The algorithm consists of two major components: (1) local object tracking module, and (2) global losing re-detection module. The local tracking module optimizes the conventional correlation filtering algorithm. Firstly, the Color Name feature is applied to increase the color sensitivity. Secondly, a scale traversal is employed to accommodate target scale changes. In the global losing re-detection module, the target losing judgment and global re-detection is realized by keypoint feature models of foreground and background. The proposed tracker achieves the 1st place in the VTB50 test set with 81.3% precision and 61.3% success rate, which outperforms other existing state-of-the-art trackers by over 10%. And it achieves the 2nd place in our Chasing-Car test set with a higher real-time performance 43.2 fps. The experimental results show that the proposed tracker has higher accuracy and robustness when dealing with situations like object deformation, occlusion and target-losing, etc.

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Section: Introductionmentioning

confidence: 99%