计算机工程 ›› 2018, Vol. 44 ›› Issue (7): 14-19.doi: 10.19678/j.issn.1000-3428.0051084

所属专题: 智能交通专题

• 智能交通专题 • 上一篇    下一篇

基于智能驾驶的动态目标跟踪研究

张晶晶 a,杨鹏 a,刘元盛 b,梁军 c   

  1. 北京联合大学 a.北京市信息服务工程重点实验室; b.机器人学院; c.工科综合实验教学示范中心,北京 100101
  • 收稿日期:2018-01-04 出版日期:2018-07-15 发布日期:2018-07-15
  • 作者简介:张晶晶(1990—),女,硕士研究生,主研方向为智能驾驶;杨鹏(通信作者),教授、博士生导师;刘元盛、梁军,教授。
  • 基金项目:
    国家自然科学基金“视听觉信息的认知计算”重大研究计划重点支持项目“智能车驾驶脑认知技术、平台与转化研究”(91420202);英国皇家工程院牛顿基金(UK-CIAPP\\324);北京联合大学人才强校优选计划项目(BPHR2017EZ02)。

Research on Dynamic Target Tracking Based on Intelligent Driving

ZHANG Jingjing a,YANG Peng a,LIU Yuansheng b,LIANG Jun c   

  1. a.Beijing Key Laboratory of Information Services Engineering; b.College of Robotics; c.Demonstration Center of Experimental Teaching in Comprehensive Engineering,Beijing Union University,Beijing 100101,China
  • Received:2018-01-04 Online:2018-07-15 Published:2018-07-15

摘要:

针对智能驾驶过程中存在背景变化剧烈、光照变化影响较大且背景颜色不易区分等缺陷,提出一种改进的卡尔曼粒子滤波算法。采用灰度投影算法对车辆视频和图像序列帧进行预处理,通过Harris角点检测在图像区域内的目标和背景提取角点,利用卡尔曼嵌入粒子滤波器对 粒子滤波进行二次预测,以保证智能驾驶过程中动态跟踪的有效性和准确性。实验结果表明,与传统KPF算法相比,该算法在不同场景下的动态目标跟踪能力明显增强,在复杂的交通驾驶环境下跟踪准确率为95.7%,且具有较好的实时性。

关键词: 目标跟踪, 智能驾驶, Harris角点检测, 卡尔曼滤波, 粒子滤波

Abstract:

Aiming at the shortcomings such as the dramatic change of background,the influence of illumination change and the indistinguishable background color in the intelligent driving process,an improved Kalman particle filter algorithm is proposed.A gray-scale projection algorithm is used to preprocess vehicle video and image sequence frames.Harris corners dertection is used to extract angles of the target and background in the image region,and Kalman-embedded particle filter is used to make a second prediction of particle filtering.The effectiveness and accuracy of dynamic tracking are ensured during smart driving.Experimental results show that compared with the traditional KPF algorithm,the Moving Object Tracking(MOT) ability of the algorithm in different scenarios is obviously enhanced,the tracking accuracy rate is 95.7% in a complex traffic driving environment,and has good real-time performance.

Key words: target tracking, intelligent driving, Harris corner detection, Kalman filtering, particle filtering

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