基于车载视觉的行人检测与跟踪方法

为提高城市交通环境下车辆主动安全性,保障行人安全,提出了基于车载视觉传感器的行人保护方法. 利用Adaboost算法实现行人的快速检测,结合Kalman滤波原理跟踪行人,以获取其运行轨迹.该方法利用离散 Adaboost算法训练样本类Haar特征,得到识别行人的级联分类器,遍历车载视觉采集的图像,以获取行人目 标;结合Kalman滤波原理,对检测到的行人目标进行跟踪,建立检测行人的动态感兴趣区域,利用跟踪结果分 析行人的运行轨迹.试验表明:该方法平均耗时约80ms/帧,检测率达到88%;结合Kalman滤波原理跟踪后,平 均耗时降到55ms/帧,实时性较好. 

Pedestrian Detection and Tracking Based on Automotive Vision

To improve the automotive active safety and guarantee the safety of pedestrians under urban transportation conditions,a pedestrian protection method based on automotive vision was presented.The Adaboost algorithm was utilized to detect pedestrians rapidly,and the Kalman filter principle was adopted to track these pedestrians and obtain their trajectories.With this method,the samples’ Haar-like features are calculated and trained by the discrete Adaboost algorithm to obtain the cascaded pedestrian recognition classifiers.These classifiers are exploited to search for pedestrians by scanning those images captured by automotive vision.The Kalman filtering principle is applied to track these pedestrians and build the dynamic region of interest for pedestrian detection.The tracking results are used to analyze their behaviors.The experimental results show that the proposed method can detect pedestrians in about 80 ms per frame with an accuracy of 88%.The time cost can reduce to 55 ms per frame after using the Kalman-based pedestrian tracking method.