Pedestrian Detection for Transformer Substation Based on Gaussian Mixture Model and YOLO

Safety is a core requirement of the transformer substation where is dangerous due to high voltage. It requires to detect pedestrians efficiently based on the surveillance video near the substation to ensure the safety of the pedestrian and the device. This paper presents a new method of pedestrian d...

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Bibliographic Details
Published in2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) Vol. 2; pp. 562 - 565
Main Authors Qiwei Peng, Wang Luo, Gongyi Hong, Min Feng, Yuan Xia, Lei Yu, Xiaolong Hao, Xu Wang, Mingxuan Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2016
Subjects
Convolution
Gaussian mixture model
GMM
Object detection
pedestrian detection
Real-time systems
Substations
Surveillance
YOLO
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Summary:Safety is a core requirement of the transformer substation where is dangerous due to high voltage. It requires to detect pedestrians efficiently based on the surveillance video near the substation to ensure the safety of the pedestrian and the device. This paper presents a new method of pedestrian detection for transformer substation based on Gaussian Mixture Model (GMM) and YOLO. We use GMM to model the background and detect the pedestrians preliminarily, at the same time, YOLO, a kind of detection method based on convolution neural network (CNN), is also applied for pedestrian detection. Through combining the two results with different weights, the network outputs a better detection result. Our extensive experiments show that the work reaches 20% higher than the single method.
DOI:10.1109/IHMSC.2016.130