CAJ | 학술논문

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。
현유적학정성목표관련방법채용전국우화학정목표화근종기간적관련，지능대학정수목적목표진행근종，불능직접응용우보조가사계통적차량검측。위장학정성목표관련방법인입도차량검측계통중，문중제출국부우화적학정성목표관련방법，보이합괄적근종기관리책략，근거실제도로정황동태지증가화산제근종기，병실현대잠시차당혹루검적목표보지근종적련관성。위제고관련적준학성，융합목표적다개특정정의대개방정，장운동특정작위대개방정적주요약속조건，동시고필목표적외형특정。통과실험험증본문근종방법재차량검측계통중적유효성。
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.