Visual Indexing of Large Scale Train-Borne Video for Rail Condition Perceiving

Peng DAI  Shengchun WANG  Yaping HUANG  Hao WANG  Xinyu DU  Qiang HAN  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.9   pp.2017-2026
Publication Date: 2017/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016PCP0020
Type of Manuscript: Special Section PAPER (Special Section on Picture Coding and Image Media Processing)
route panorama,  video indexing,  train-borne video,  train-swaying,  track irregularity,  

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Train-borne video captured from the camera installed in the front or back of the train has been used for railway environment surveillance, including missing communication units and bolts on the track, broken fences, unpredictable objects falling into the rail area or hanging on wires on the top of rails. Moreover, the track condition can be perceived visually from the video by observing and analyzing the train-swaying arising from the track irregularity. However, it's a time-consuming and labor-intensive work to examine the whole large scale video up to dozens of hours frequently. In this paper, we propose a simple and effective method to detect the train-swaying quickly and automatically. We first generate the long rail track panorama (RTP) by stitching the stripes cut from the video frames, and then extract track profile to perform the unevenness detection algorithm on the RTP. The experimental results show that RTP, the compact video representation, can fast examine the visual train-swaying information for track condition perceiving, on which we detect the irregular spots with 92.86% recall and 82.98% precision in only 2 minutes computation from the video close to 1 hour.