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Open AccessArticle

TIR-MS: Thermal Infrared Mean-Shift for Robust Pedestrian Head Tracking in Dynamic Target and Background Variations

Department of Electronics Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan-si 38541, Korea
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Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(15), 3015; https://doi.org/10.3390/app9153015
Received: 29 May 2019 / Revised: 16 July 2019 / Accepted: 19 July 2019 / Published: 26 July 2019
(This article belongs to the Special Issue Intelligence Systems and Sensors)
Thermal infrared (TIR) pedestrian tracking is one of the major issues in computer vision. Mean-shift is a powerful and versatile non-parametric iterative algorithm for finding local maxima in probability distributions. In existing infrared data, and mean-shift-based tracking is generally based on the brightness feature values. Unfortunately, the brightness is distorted by the target and background variations. This paper proposes a novel pedestrian tracking algorithm, thermal infrared mean-shift (TIR-MS), by introducing radiometric temperature data in mean-shift tracking. The thermal brightness image (eight-bits) was distorted by the automatic contrast enhancement of the scene such as hot objects in the background. On the other hand, the temperature data was unaffected directly by the background change, except for variations by the seasonal effect, which is more stable than the brightness. The experimental results showed that the TIR-MS outperformed the original mean-shift-based brightness when tracking a pedestrian head with successive background variations. View Full-Text
Keywords: pedestrian tracking; infrared; temperature; brightness; background contrast; radiometry; mean shift pedestrian tracking; infrared; temperature; brightness; background contrast; radiometry; mean shift
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MDPI and ACS Style

Yun, S.; Kim, S. TIR-MS: Thermal Infrared Mean-Shift for Robust Pedestrian Head Tracking in Dynamic Target and Background Variations. Appl. Sci. 2019, 9, 3015.

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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3234511
    Link: https://zenodo.org/deposit/3234511
    Description: We evaluated the tracking performance of the proposed method and attached a video with the results of the tracking. We used to summarized as tracking performance based Average Histogram Similarity, Central Difference and Intersection Over Union (IOU), respectively.
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