基于改进的确定性目标关联的车辆跟踪方法

摘要

现有的确定性目标关联方法采用全局优化确定目标和跟踪器间的关联，只能对确定数目的目标进行跟踪，不能直接应用于辅助驾驶系统的车辆检测。为将确定性目标关联方法引入到车辆检测系统中，文中提出局部优化的确定性目标关联方法，辅以合适的跟踪器管理策略，根据实际道路情况动态地增加和删除跟踪器，并实现对暂时遮挡或漏检的目标保持跟踪的连贯性。为提高关联的准确性，融合目标的多个特征定义代价方程，将运动特征作为代价方程的主要约束条件，同时考虑目标的外形特征。通过实验验证本文跟踪方法在车辆检测系统中的有效性。%The existing deterministic data association defines the optimal track set by global optimization algorithms, which can only work with prior knowledge of the objects number. Therefore, it is limited to vehicle detection in driver assistance system. A local optimization algorithm is proposed to implement deterministic correspondence in vehicle detection. With the aid of a proper tracker management strategy, the algorithm can handle object entries, object exits and occlusions. To improve the accuracy of correspondence, the cost function is defined by fusing multiple features. The motion feature and the figuration features are taken as main constraint condition and minor ones respectively. The validity of the tracking algorithm in vehicle detection system is verified by experiments.