基于高速公路场景的车辆目标跟踪

Vehicle Object Tracking Method Based on Highway Scenario

车辆目标检测与跟踪是高速公路视频监控系统实时监控获取交通参数的关键步骤.本文提出了一种面向高速公路场景的目标轨迹时序信息结合核相关滤波KCF算法的车辆目标跟踪方法,实现了车辆目标的高精度持续跟踪.该方法首先采用基于深度学习的单目标检测SSD算法,通过建立车辆数据集,实现了适用于高速公路场景的车辆目标的分类与检测.然后,基于目标轨迹时序信息实现目标车辆与轨迹的匹配,并且采用KCF跟踪算法对丢失目标进行预测重定位,从而实现车辆目标轨迹的持续跟踪.实验表明,该跟踪方法精度高,且适应多种不同场景,具有较高的应用价值.

Vehicle object detection and tracking is a key step for the real-time monitoring and acquisition of traffic parameters in the video monitoring system of expressway. A vehicle tracking method based on trajectory temporal information with KCF algorithm is proposed to realize high precision continuous tracking. Firstly, data sets is established, the classification and detection of vehicle applicable to highway scenario are realized by using SSD algorithm based on deep learning. Then, based on the trajectory of temporal information, matching between object and trajectory is realized, and KCF algorithm is applied to forecast the missing object positioning, so as to realize the vehicle trajectory tracking. The experiment result shows that this tracking method has high precision, can adapt to many different scenarios, thus has high application value.