Computer Science ›› 2015, Vol. 42 ›› Issue (4): 285-291.doi: 10.11896/j.issn.1002-137X.2015.04.059

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Localization of Causing-traffic-trouble Vehicle with Multi-level Cascaded Visual Attention Model

CHAI Zhen-liang and ZANG Di   

  • Online:2018-11-14 Published:2018-11-14

Abstract: The localization of causing-traffic-trouble vehicle is one of the most key problems for intelligent transportation system (ITS).This paper proposed a multi-level cascaded visual attention model to localize the causing-traffic-trouble vehicle.In each level of the proposed model,one significant feature of the vehicle such as color or vehicle logo is extracted and compared to the vehicle which has caused an accident.Then vehicles that have no similar features can be filtered.By performing feature extraction and feature comparison for several times,only the causing-traffic-trouble vehicle will be left behind.The experimental results demonstrate that the proposed approach is able to locate the causing-traffic-trouble vehicle accurately and is robust to luminance and noise.

Key words: Causing-traffic-trouble vehicle,Vehicle matching,Computer vision,Visual attention model,Plate recognition

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