Fusion of thermal infrared and visible spectrum for robust pedestrian tracking

2014 ◽  
Author(s):  
Moulay A. Akhloufi ◽  
Celia Porcher ◽  
Abdelhakim Bendada
Keyword(s):  
Visible Spectrum ◽  
Thermal Infrared ◽  
Pedestrian Tracking

A new thermal infrared and visible spectrum images-based pedestrian detection system

2018 ◽  
Vol 78 (12) ◽  
pp. 15861-15885 ◽  
Author(s):  
Redouan Lahmyed ◽  
Mohamed El Ansari ◽  
Ayoub Ellahyani
Keyword(s):  
Detection System ◽  
Pedestrian Detection ◽  
Visible Spectrum ◽  
Thermal Infrared

scholarly journals Reconstructing Face Image from the Thermal Infrared Spectrum to the Visible Spectrum

Sensors ◽  
2016 ◽  
Vol 16 (4) ◽  
pp. 568 ◽  
Author(s):  
Brahmastro Kresnaraman ◽  
Daisuke Deguchi ◽  
Tomokazu Takahashi ◽  
Yoshito Mekada ◽  
Ichiro Ide ◽  
...  
Keyword(s):  
Infrared Spectrum ◽  
Visible Spectrum ◽  
Thermal Infrared ◽  
Face Image

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

2019 ◽  
Vol 9 (15) ◽  
pp. 3015 ◽  
Author(s):  
Sungmin Yun ◽  
Sungho Kim
Keyword(s):  
Probability Distributions ◽  
Mean Shift ◽  
Thermal Infrared ◽  
Temperature Data ◽  
Seasonal Effect ◽  
Head Tracking ◽  
Pedestrian Tracking ◽  
Local Maxima ◽  
Dynamic Target ◽  
Feature Values

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.

scholarly journals PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark

2020 ◽  
Vol 22 (3) ◽  
pp. 666-675 ◽  
Author(s):  
Qiao Liu ◽  
Zhenyu He ◽  
Xin Li ◽  
Yuan Zheng
Keyword(s):  
Thermal Infrared ◽  
Pedestrian Tracking
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