Volume 39 Issue 2
Apr.  2021
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Article Navigation >  Journal of Transport Information and Safety  > 2021  >  39(2): 95-100, 108
ZHOU Jun, BAO Xu, GAO Yan, LI Yun, JIANG Qing. A Vehicle Detecting Method Based on Pattern Recognition Combined with ST-MRF[J]. Journal of Transport Information and Safety, 2021, 39(2): 95-100, 108. doi: 10.3963/j.jssn.1674-4861.2021.02.012
Citation: ZHOU Jun, BAO Xu, GAO Yan, LI Yun, JIANG Qing. A Vehicle Detecting Method Based on Pattern Recognition Combined with ST-MRF[J]. Journal of Transport Information and Safety, 2021, 39(2): 95-100, 108. doi: 10.3963/j.jssn.1674-4861.2021.02.012
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A Vehicle Detecting Method Based on Pattern Recognition Combined with ST-MRF

doi: 10.3963/j.jssn.1674-4861.2021.02.012

  • Faculty of Transportation Engineering, Huaiyin Institute of Technology, Huai'an 223003, Jiangsu, China

  • Received Date: 2020-07-07
    Abstract
  • The main difficulty of the vehicle detecting technology lies in overcoming the inaccuracy of detecting, which is directly affected by video shaking, mutual occlusion between vehicles, occlusion between the vehicles, and its shadow due to illumination changes. Based on the traditional spatial-temporal Markov random field (ST-MRF), the vehicle detecting method combined with pattern recognition and ST-MRF is proposed to address the problem. The boundary between two vehicles occluding each other is segmented by the pattern recognition technology, with the edge clearances and boundary information of occluded vehicles identified. Then, the results of pattern recognition are fed back to ST-MRF algorithm, which reassigns labels to occluded vehicles, integrates incomplete segmentation, and determines individual vehicle information. The results in the road section show 325 vehicles driving in the test area, vehicles tracked by the original ST-MRF algorithm, and the success rate is 79%. When the pattern recognition technology combined with ST-MRF algorithm to calculate 315 vehicles, the success rate is 97%. The results at the intersection show that the method can obtain more accurate results of vehicle detection at the intersection where motor vehicles and non-motor vehicles are mixed, and buses and cars are obstructed by each other.

     

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