融合热释电红外传感器与视频监控器的多目标跟踪算法

现有基于热释电红外传感器的多目标跟踪系统在目标之间距离较近或者轨迹相交的情况下存在着误差较大的缺点.针对此缺点，提出了一种新型的基于热释电红外传感器与视频监测器协同工作的多目标跟踪方案.该方案可以充分利用两种传感器的优势，弥补在目标跟踪中的不足.算法采用最小二乘法利用热释电信息进行定位,并通过从图像或热释电传感器信号的幅频特性中提取特征信息来校正联合概率数据关联算法的关联矩阵，有效避免了错误关联.实验表明，该方案在多目标交叉情况下跟踪误差仅为其它算法的八分之一到四分之一.

Multi-Object Tracking Scheme with Pyroelectric Infrared Sensor and Video Camera Coordination

The error tends to be significant in many existing pyroelectric infrared sensor based multi-object tracking systems when the measured objects get close to each other or their trajectories have intersections.To solve this problem,we proposed a multi-object tracking scheme by having pyroelectric infrared sensors and video cameras work cooperatively.This scheme takes the advantages of both kinds of sensors,which help to improve the performance compared to those using any kind of such sensors.In the proposed scheme,we first achieve coarse positioning using least square method with data collected by pyroelectric infrared sensors,and then we correct the incidence matrix in joint probabilistic data association with features extracted from the images or the frequency responses of pyroelectric sensors.The coarse positioning is further filtered by joint probabilistic data association algorithm to obtain the final fine result.Such a method prevents false association effectively.Experimental results show that the tracking error of the proposed scheme in multi-object crossover scenario reduces to a quarter,even to one eighth of the errors that exist in the compared schemes.