基于OGS-DTW算法的交通事件自动检测方法

Traffic Event Automatic Detection Based on OGS-DTW Algorithm

为提高交通事件检测方法的综合性能,使用动态背景更新和改进的运动估计,将动态图像序列转化为车辆标号场,实现对车辆的跟踪;构造轨迹建模和编码,提取车辆的运动轨迹,并建立自组织神经网络进行行为模式学习;最后,使用OGS-DTW算法对轨迹数据进行预处理,并对距离函数进行求解,从而实现待测事件序列的轨迹与典型轨迹数据模式的匹配。分别以U形转、违章左拐和违章变道3种事件为对象进行了多组对比,检测成功率均在80%以上。试验还进行3种检测方法指标的对比,在平均耗时方面,一般的DTW算法、改进的DTW算法及基于OGS改进的DTW算法分别是126.5、62.5、69.8 s;而它们的事件检测成功率分别是84.6%、68.8%和88.3%。结果表明:基于OGS-DTW算法的交通事件检测方法稳定且可靠,在显著降低计算量的同时,仍然保证了较高的匹配准确性,成功率高、实时性好。

In order to improve the comprehensive performance of the automatic traffic event detection,the dynamic background updating and the improved motion estimating algorithm were used to convert the dynamic image sequence into vehicle map sequence to realize the vehicle tracking.Then,the trajectory modeling and encoding methods were constructed to extract the motion trajectory of the vehicle.Self-organizing neural networks were also built up to learn the typical pattern of the motion trajectories.Finally,the OGS-DTW algorithm was used to preprocess the data of motion trajectory and compute the distance function,so that the match between trajectory of testing event sequence and pattern of typical trajectory data can also be realized.Groups of experiments on U-turn,illegal left-turns and illegal changing-lane were carried out,the success ratio of event detection was above 80%.The three detection methods were compared in respect of the average time-consuming and the success rate.The average time-consuming of the general DTW algorithm,the improved DTW algorithm and OGS-based improved DTW algorithm are 126.5 s,62.5 s and 69.8 s respectively,but the success rate of the events testing are 84.6%,68.8% and 88.3% respectively.The experiment results show that the traffic event detection algorithm based on OGS-DTW is reliable and stable,which has higher matching accuracy when reducing computation.The event detection algorithm has higher success ratio and good real-time performance.