An effective approach to pedestrian detection in thermal imagery

Li, Wei; Zheng, Dequan; Zhao, Tiejun; Mengda, Yang

doi:10.1109/icnc.2012.6234621

Cited by 37 publications

(40 citation statements)

References 9 publications

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“…They also tested their method on the OSU thermal database [18]. Li et al [2] implemented the pedestrian detection in infrared imagery by tuning HOG features. They also tested their algorithm on the OSU thermal pedestrian dataset [18].…”

Section: Thermal Approachesmentioning

confidence: 99%

“…Visual cameras capturing visible light, as well as thermal cameras capturing infrared radiation have been utilized for person detection. Many feature based machine learning [1][2][3][4], as well as deep learning [5][6][7] approaches have been utilized to deal with the problem of person detection in thermal images. Even though thermal cameras have an advantage in outdoor person detection, due to the independence of illumination, robust detection still becomes very challenging in diverse weather and light conditions (see Figure 1) and is therefore far from a solved problem.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of a Diverse Dataset for Transfer Learning in Thermal Person Detection

Huda

Hansen

Gade

et al. 2020

Sensors

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Thermal cameras are popular in detection for their precision in surveillance in the dark and for privacy preservation. In the era of data driven problem solving approaches, manually finding and annotating a large amount of data is inefficient in terms of cost and effort. With the introduction of transfer learning, rather than having large datasets, a dataset covering all characteristics and aspects of the target place is more important. In this work, we studied a large thermal dataset recorded for 20 weeks and identified nine phenomena in it. Moreover, we investigated the impact of each phenomenon for model adaptation in transfer learning. Each phenomenon was investigated separately and in combination. the performance was analyzed by computing the F1 score, precision, recall, true negative rate, and false negative rate. Furthermore, to underline our investigation, the trained model with our dataset was further tested on publicly available datasets, and encouraging results were obtained. Finally, our dataset was also made publicly available.

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Section: Thermal Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Effect of a Diverse Dataset for Transfer Learning in Thermal Person Detection

Huda

Hansen

Gade

et al. 2020

Sensors

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“…Researchers also have developed different techniques in Thermal Imaging Wei Li, Dequan Zheng, Tiejun Zhao, engda Yang [10], have used combination of the HOG features with geometric characteristics for object detection along with SVM classifiers. Marco San-Biaggio Marco Crocco Marco Cristani, as in [11] have used Recursive Segmentation (Using adaptive thresholding) to detect ROl's in Thermal Images.…”

Section: Literature Reviewmentioning

confidence: 99%

Moving Object Detection using Visible Spectrum Imaging and Thermal Imaging

Mangale

Khambete²

2015

2015 International Conference on Industrial Instrumentation and Control (ICIC)

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This paper deals with development of Robust Moving Object Detection method using Visible Spectrum and Thermal Imaging. If we fuse visible spectrum and Thermal imaging together, more information about the moving object can be obtained as both are inherently complementary to each other. The segmentation of moving objects in corresponding video frames involves 'Background Subtraction' method. Here each pixel of visible as well as thermal is modeled as mixture of Gaussians for background subtraction, then blob analysis and fusion rule is applied to build Robust moving Object detection system.

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“…The thermal cameras can here often be a better choice than a normal visual camera. The methods applied to thermal imaging span from simple thresholding and shape analysis [43,17,39,15,7] to more complex, but well-known methods such as HOG and SVM [42,37,41,31,26] as well as contour analysis [10,9,27,38]. Using simple methods allows for fast real-time processing, and combined with the illumination independency, the thermal sensor is very well suited for detecting humans in real-life applications.…”

Section: Related Workmentioning

confidence: 99%