Lie群下利用改进JPDA滤波器的智能车立体视觉多目标跟踪方法

提出基于改进联合概率数据关联滤波器的智能车立体视觉多目标跟踪方法。利用立体视觉摄像头采集车辆及行人图像、视频；在Lie群下对传感器的不确定性进行建模，并采用欧几里德群算法对预处理的图像进行状态滤波；在可能存在车辆的区域内利用双目视觉去除误检，并获得车辆的位置信息；通过卡尔曼滤波器对测量的不确定度和预测目标运动的轨迹进行确认；运用改进的联合概率数据关联滤波器对车辆及行人的跟踪结果进行优化修正。实验结果表明，提出的方法可以有效解决智能车多目标跟踪问题，大幅度提升驾驶系统的自动化和智能化水平。相比其他较新的目标跟踪方法，提出的方法在跟踪精度和速度上具有明显的优势，且在跟踪车辆时不会产生明显的偏移、不会遗漏对行人的跟踪。

Stereo vision multi-object tracking for intelligent vehicle using joint probabilistic data association in Lie group

This paper proposed a stereo vision multi-object tracking method based on joint probabilistic data association(JPDA) filter for intelligent vehicle. This method collected the images and videos of vehicles and pedestrians with the help of stereo camera mounted on top of a vehicle. It modelled the uncertainty of sensor in Lie group and adopted the Euclidean algorithm to implement state-filtering for the preprocessed images. It utilized the improved JPDA to rectify the tracking of vehicles and pedestrians. Experiment results show that the proposed method can settle the tracking of multi-objects effectively, and improve the level of automation and intelligence for driving system dramatically. Compared with other new target tracking methods, the proposed method has obvious advantages in tracking precision and speed. It does not produce obvious offset in tracking the vehicle and will not miss the tracking of pedestrians.