NDLI: Visual abnormality detection framework for train-mounted pantograph headline surveillance

Content Provider	IEEE Xplore Digital Library
Author	Peng Tang Weidong Jin Liang Chen
Copyright Year	2014
Description	Author affiliation: Sch. of Electr. Eng., Southwest Jiaotong Univ., Chengdu, China (Peng Tang; Weidong Jin) \|\| Chengdu Monitoring Station, State Radio Monitoring Center, Chengdu, China (Liang Chen)
Abstract	Computer vision enhanced automatic routing inspection and monitoring for railway pantograph headlines is a promising technical orientation to reduce manual operations. The vision based approach, which comply exactly with human cognition, draws many researcher's attention because of its informative nature. However, due to the dimension collapse in photogenic process that is essentially an ill-posed problem, to automatically detect and locate the abnormal visual pattern and structure in railway inspection videos is still a challenging task. We propose a visual abnormality detection framework by combining the appearance, scale and location of visual patterns via a probabilistic Bayesian approach. The poles and supporting arms of power supply lines are detected firstly to yield the region of interest for detailed processing. Then the hypothesis of potential abnormal pattern are collected from the endpoints extracted from local curve and line segments, so as to strengthen the perceptual difference. After that, the abnormality model and background model are implemented to classify the candidates in hypothesis. Finally, promising experimental results demonstrate the potentials of the proposed abnormal detection method with respect to various insignificant patterns and cluttered backgrounds.
Starting Page	847
Ending Page	852
File Size	1587873
Page Count	6
File Format	PDF
ISBN	9781479960781
DOI	10.1109/ITSC.2014.6957795
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Rail transportation Feature extraction Inspection Monitoring Power supplies Cameras
Content Type	Text
Resource Type	Article

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