计算机科学 ›› 2017, Vol. 44 ›› Issue (3): 288-295.doi: 10.11896/j.issn.1002-137X.2017.03.059

• 图形图像与模式识别 • 上一篇    下一篇

基于智能手机的车辆行为实时判别与渐进矫正方法研究

范菁,吴青青,叶阳,董天阳   

  1. 浙江工业大学计算机科学与技术学院 杭州310023,浙江工业大学计算机科学与技术学院 杭州310023,浙江工业大学计算机科学与技术学院 杭州310023,浙江工业大学计算机科学与技术学院 杭州310023
  • 出版日期:2018-11-13 发布日期:2018-11-13
  • 基金资助:
    本文受浙江省重大科技专项重大工业项目(2013C01112)资助

Real-time Determination and Progressive Correction of Vehicle Behavior on Smartphones

FAN Jing, WU Qing-qing, YE Yang and DONG Tian-yang   

  • Online:2018-11-13 Published:2018-11-13

摘要: 目前基于智能手机的车辆行为识别算法存在着鲁棒性较差、识别率较低、无法应用于实时行驶判断等问题。针对上述问题,提出了基于智能手机的车辆行为实时判别与渐进矫正方法,以提高车辆行为识别的准确率和实时性。该方法利用车辆行为发生时存在的渐进变化数据来进行车辆行为的识别与渐进矫正分类,并通过采集过程数据作为分类器训练样本,提高支持向量机(SVM)分类器的车辆行为识别和预测能力。同时,针对传统滑动窗口检测的局限性,该方法采用了端点检测算法,从而能快速地从车辆行驶数据中截取并识别行为轨迹信息,以减少车辆行为的误判。实验结果表明,基于时间分段矫正的行为识别算法能够有效地对车辆行为进行预测,并最终达到较高的识别率,证明了该方法的有效性。

关键词: 车辆行为识别,SVM,实时判别

Abstract: Up to now,the relevant research has some drawbacks:poor robustness,low accuracy rate and non-real time.To solve these problems,a vehicle behavior recognition algorithm of real-time determination and progressive correction on smartphone was proposed.This algorithm classifies vehicle behavior by the data generated during driving process,and uses the collected data as training samples to improve recognition and prediction capability of SVM.For the limitations of traditional sliding window,the endpoint detection algorithm is used to quickly extract useful information from the complete vehicle behavior,which reduces misjudgment simultaneously.The experimental results show that corrective algorithm on time-based segmentation can effectively predict the vehicle behavior,and ultimately achieve high re-cognition rate,which demonstrates the effectiveness of this method.

Key words: Vehicle behavior recognition,SVM,Real-time determination

[1] Piciarelli C,Micheloni C,Foresti G L.Trajectory-Based Anomalous Event Detection[J].IEEE Transactions on Circuits & Systems for Video Technology,2008,18(11):1544-1554.
[2] KAMIJO S,MATSUSHITA Y,IKEUCHI K,et al.Traffic monitoring and accident detection at intersections[J].IEEE Transa-ctions on Intelligent Transportation Systems,2000,1(2):108-118.
[3] KAMRAN S,HAAS O.A Multilevel Traffic Incidents Detection Approach:Identifying Traffic Patterns and Vehicle Behaviours using real-time GPS data[C]∥Intelligent Vehicles Symposium,2007 IEEE.IEEE,2007:912-917.
[4] LY M V,MARTIN S,TRIVEDI M M.Driver classification and driving style recognition using inertial sensors[C]∥Intelligent Vehicles Symposium (IV),2013 IEEE.IEEE,2013:1040-1045.
[5] REDDY S,MUN M,BURKE J,et al.Using mobile phones to determine transportation modes[J].ACM Transactions on Sensor Networks,2010,6(2):662-701.
[6] FENG T,TIMMERMANS H J P.Transportation mode recognition using GPS and accelerometer data[J].Transportation Research Part C Emerging Technologies,2013,37(3):118-130.
[7] JOHNSON D A,TRIVEDI M M.Driving style recognition using a smartphone as a sensor platform[C]∥2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).IEEE,2011:1609-1615.
[8] WANG X H,CONG Z H,FANG L L,et al.Determination of Real-time Vehicle Driving Status Using HMM[J].Acta Automatica Sinica,2013,39(12):2131-2142.(in Chinese) 王相海,丛志环,方玲玲,等.基于HMM的车辆行驶状态实时判别方法研究[J].自动化学报,2013,39(12):2131-2142.
[9] HAO J Y,HAO S,LI C,et al.Vehicle behavior understanding based on movement string[C]∥12th International IEEE Conference on Intelligent Transportation Systems,2009(ITSC’09).IEEE,2009:1-6.
[10] LAWSON C L,HANSON R J.Solving least squares problems[M].Englewood Cliffs,NJ:Prentice-hall,1974.
[11] ANTONINI M,BARLAUD M,MATHIEU P,et al.Image co-ding using wavelet transform[J].IEEE Transactions on Image Processing,1992,1(2):205-220.
[12] JOLLIFFE I.Principal component analysis[M].John Wiley & Sons,Ltd,2002.
[13] HEARST M A,DUMAIS S T,OSMAN E,et al.Support vector machines[J].Intelligent Systems and their Applications,IEEE,1998,13(4):18-28.
[14] CHANG C C,LIN C J.LIBSVM:A library for support vector machines[J].ACM Transactons on Intelligent Systems & Technology,2007,2(3):389-396.
[15] SHEN J,HUNG J,LEE L.Robust entropy-based endpoint detection for speech recognition in noisy environments[C]∥ICSLP.1998,98:232-235.

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