一种邻帧匹配与卡尔曼滤波相结合的多目标跟踪算法

A Multi-object Tracking Algorithm Based on the Combination of Neighborhood Frame Matching and Kalman Filtering

传统目标跟踪算法很难在户外视频中进行行人实时跟踪及人流量统计,针对该问题,提出一种联合邻帧匹配与卡尔曼滤波器的多目标跟踪算法。该算法首先以方向梯度直方图(HOG)为特征的自适应推进(Adaboost)分类器进行行人人头检测,进而采用卡尔曼滤波进行行人轨迹预测,并应用邻帧匹配算法对该目标运动轨迹进行校正。在邻帧匹配法中,新颖地将跟踪过程中的目标匹配问题转化为基于匈牙利算法的指派问题。因邻帧匹配法能更好地区分不同待匹配目标的特征,较传统跟踪算法,能更好地避免误匹配、漏匹配等问题,提高了目标跟踪的准确率,实验证明,该监控系统准确率高,且能满足扶梯监控系统应用场景下的鲁棒性、实时性要求。

In order to segment pedestrians in the crowded scene of real time video,a pedestrian tracking algorithm is proposed,which combine neighborhood frame matching and kalman filtering. Firstly,gradient histogram( HOG) combined with adaptive boosting( Adaboost) were used to detect the passenger heads in the scene. Then,neighborhood frame matching method combined with Kalman filter were used for multi-target tracking. What’s more,the target matching problem is transformed into assignment problem based on Hungary algorithm. For the adjacent frames matching method can better distinguish the characteristics of different target,compared to traditional tracking algorithms,it can better avoid false matching and miss matching,improves the accuracy of target tracking.Experiments show that the algorithm is of high accuracy,and can meet the requirements of robustness and real-time performance in the application of video monitoring system.