基于特征点光流和卡尔曼滤波的运动车辆跟踪

Tracking Method for Moving Vehicles Based on Feature-Point Optical Flow and Kalman Filtration

提出一种基于特征点光流的运动目标跟踪方法,将卡尔曼滤波跟踪技术和多边形跟踪策略应用于智能交通系统的运动车辆实时跟踪,使跟踪变得简单和稳定.计算机仿真结果表明,采用所提出的方法,能够以较高精度实现运动目标跟踪的目标模型选择、目标特征点选取、特征点光流计算、特征点光流聚类和目标识别,且计算量小、易于实现.

A tracking method for motion objects is proposed based on the feature-point optical flow, which is then applied to the real-time tracking of moving vehicles in the intelligent traffic system. In the proposed method, the Kalman filtration and the polygonal tracking strategy are adopted, thus simplifying and stabilizing the tracking. Si-mulated results indicate that the proposed method can accurately determine the maneuvering target, extract the feature points, implement the computing and clustering of the feature-point optical flow and recognize moving objects. Moreover, it is of less computation and is easy to implement.