Passenger detection and tracking algorithm based on vehicle video surveillance

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (12): 3521-3525.

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Passenger detection and tracking algorithm based on vehicle video surveillance

XIE Lu¹,JIN Zhigang¹,WANG Ying¹,²

1. School of Electronic and Information Engineering, Tianjin University, Tianjin 300072, China;
2. Unit 61660, Beijing 100089, China

Received:2014-06-30 Revised:2014-08-12 Online:2014-12-31 Published:2014-12-01
Contact: XIE Lu

基于车载视频监控的乘客检测及跟踪算法

谢璐¹,金志刚¹,王颖¹,²

1. 天津大学电子信息工程学院,天津 300072
2. 61660部队，北京 100089

通讯作者: 谢璐
作者简介:谢璐(1989-）,女,河北石家庄人,硕士研究生,主要研究方向:车载视频监控、图像处理；金志刚(1972-）,男,上海人,教授,博士生导师,博士,主要研究方向:无线与水下网络协议、无线网络安全、网络视频技术；王颖(1977-）,女,河北张家口人,博士,主要研究方向:网络安全、信息保密。
基金资助:
国家自然科学基金资助项目;天津市软件产业发展专项基金资助项目

Abstract

Abstract:

Concerning the problem of barrier among passengers and unstable illumination on the bus, a detection and tracking algorithm was proposed based on edge feature and local invariant feature of head-shoulder. Firstly, the algorithm used adaptive threshold background subtraction method to achieve passenger segmentation. Secondly, it used Histogram of Oriented Gradient (HOG) feature of different sample sets to train Support Vector Machine (SVM) classifiers, and combined Adaptive Boosting (AdaBoost) algorithm to extract a strong classifier. And then it scanned the foreground using strong classifier to achieve passenger detection. Lastly, it extracted Speeded-Up Robust Feature (SURF) of target region and current search region, and then matched feature points to achieve passenger tracking. The experimental results show that this algorithm has detection rate and tracking rate of more than 80% in the case of barrier among passengers and unstable illumination, and it can meet the requirement of real-time. It can be used for passenger flow counting.

摘要：

针对公交车上乘客相互遮挡及光照变化明显的问题,提出一种基于头肩部边缘特征和局部不变特征的人体检测及跟踪算法。首先对待检测图像进行自适应阈值背景差分,实现乘客目标分割；然后用样本的梯度方向直方图(HOG)特征训练支持向量机(SVM)基础分类器,结合自适应增强(AdaBoost)算法提炼出最终的强分类器,对前景图像进行扫描实现乘客目标检测；最后提取目标区域和当前搜索区域的快速鲁棒性特征(SURF),通过特征点匹配实现乘客目标跟踪。实验表明,在乘客相互遮挡及光照变化明显的情况下,该算法仍具有高于80%的检测率和跟踪率,且满足系统实时性的要求,可用于客流计数。

CLC Number:

TP391.4

XIE Lu JIN Zhigang WANG Ying. Passenger detection and tracking algorithm based on vehicle video surveillance[J]. Journal of Computer Applications, 2014, 34(12): 3521-3525.

谢璐金志刚王颖. 基于车载视频监控的乘客检测及跟踪算法[J]. 计算机应用, 2014, 34(12): 3521-3525.

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Passenger detection and tracking algorithm based on vehicle video surveillance

基于车载视频监控的乘客检测及跟踪算法

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