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  • ISSN 1006-3080
  • CN 31-1691/TQ

基于类Haar特征和AdaBoost的车辆识别技术

张雪芹 方婷 李志前 董明杰

张雪芹, 方婷, 李志前, 董明杰. 基于类Haar特征和AdaBoost的车辆识别技术[J]. 华东理工大学学报(自然科学版), 2016, (2): 260-265. doi: 10.14135/j.cnki.1006-3080.2016.02.017
引用本文: 张雪芹, 方婷, 李志前, 董明杰. 基于类Haar特征和AdaBoost的车辆识别技术[J]. 华东理工大学学报(自然科学版), 2016, (2): 260-265. doi: 10.14135/j.cnki.1006-3080.2016.02.017
ZHANG Xue-qin, FANG Ting, LI Zhi-qian, DONG Ming-jie. Vehicle Recognition Technology Based on Haar-Like Feature and AdaBoost[J]. Journal of East China University of Science and Technology, 2016, (2): 260-265. doi: 10.14135/j.cnki.1006-3080.2016.02.017
Citation: ZHANG Xue-qin, FANG Ting, LI Zhi-qian, DONG Ming-jie. Vehicle Recognition Technology Based on Haar-Like Feature and AdaBoost[J]. Journal of East China University of Science and Technology, 2016, (2): 260-265. doi: 10.14135/j.cnki.1006-3080.2016.02.017

基于类Haar特征和AdaBoost的车辆识别技术

doi: 10.14135/j.cnki.1006-3080.2016.02.017
基金项目: 

国家自然科学基金(61371150)

详细信息
  • 中图分类号: TP391.41

Vehicle Recognition Technology Based on Haar-Like Feature and AdaBoost

  • 摘要: 在海量的监控视频中,快速、准确地识别车辆对公安破案和追踪具有重要的研究意义。通过提取车辆的类Haar特征,采用AdaBoost方法构建分类器可以实现监控视频中的车辆识别。针对原始算法误检率较高的问题,提出了采用背景差分去除背景干扰,以及采用目标对象差分法进行二次识别的两种改进算法。实验结果表明,两种改进算法都能够有效地降低误检率,提高检测率,并且对不同交通场景下的监控视频具有很好的检测效果。

     

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出版历程
  • 收稿日期:  2015-07-17
  • 刊出日期:  2016-04-29

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