Weighted HOG for Thermal Pedestrian Detection

Li, Yi-Hao; Huang, Shih-Shinh; Lu, Ching-Hu; Chang, Feng-Chia

doi:10.1109/icce-china.2018.8448810

Cited by 2 publications

(1 citation statement)

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“…The method involves computing of the gradient orientation in each section of the image divided by a dense grid of evenly distributed cells (detection windows/ROIs) [8]. HOG-based algorithms are used, among other for the pedestrian detection [41,42], including applications based on thermovision digital images [43]. The sequence of the operations to be performed in order to detect objects with use of HOG descriptor, is presented in the right dashed frame in the Figure 9.…”

Section: Hog Algoritmmentioning

confidence: 99%

Application of the Infrared Thermography and Unmanned Ground Vehicle for Rescue Action Support in Underground Mine—The AMICOS Project

et al. 2020

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Extraction of raw materials, especially in extremely harsh underground mine conditions, is irrevocably associated with high risk and probability of accidents. Natural hazards, the use of heavy-duty machines, and other technologies, even if all perfectly organized, may result in an accident. In such critical situations, rescue actions may require advanced technologies as autonomous mobile robot, various sensory system including gas detector, infrared thermography, image acquisition, advanced analytics, etc. In the paper, we describe several scenarios related to rescue action in underground mines with the assumption that searching for sufferers should be done considering potential hazards such as seismic, gas, high temperature, etc. Thus, possibilities of rescue team activities in such areas may be highly risky. This work reports the results of testing of a UGV robotic system in an underground mine developed in the frame of the AMICOS project. The system consists of UGV with a sensory system and image processing module that are based on an adaptation of You Only Look Once (YOLO) and Histogram of Oriented Gradients (HOG) algorithms. The experiment was very successful; human detection efficiency was very promising. Future work will be related to test the AMICOS technology in deep copper ore mines.

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Section: Hog Algoritmmentioning

confidence: 99%