CAJ | 학술논문

车辆跟踪作为智能交通系统中的一项关键技术备受广大学者关注.SIFT(scale-invariant feature trans-form)特征可以有效解决目标的旋转、缩放、平移,为车辆跟踪提供很好的特征支持,但是传统的SIFT特征跟踪不能区分前景和背景,极多的匹配特征集中在背景上,导致跟踪目标丢失.在研究现有车辆跟踪算法的基础上,提出了基于SIFT特征与GrabCut算法的车辆跟踪方法,SIFT特征有效解决了车辆姿态变化及远近变化问题,GrabCut算法有效保证了前景及背景的准确分割.实验表明,该方法在日间摄像机不明显晃动环境下,初始帧运动检测车辆后能够对运动车辆实现稳定的跟踪,并且有效解决了车辆姿态变化及远近变化问题.
차량근종작위지능교통계통중적일항관건기술비수엄대학자관주.SIFT(scale-invariant feature trans-form)특정가이유효해결목표적선전、축방、평이,위차량근종제공흔호적특정지지,단시전통적SIFT특정근종불능구분전경화배경,겁다적필배특정집중재배경상,도치근종목표주실.재연구현유차량근종산법적기출상,제출료기우SIFT특정여GrabCut산법적차량근종방법,SIFT특정유효해결료차량자태변화급원근변화문제,GrabCut산법유효보증료전경급배경적준학분할.실험표명,해방법재일간섭상궤불명현황동배경하,초시정운동검측차량후능구대운동차량실현은정적근종,병차유효해결료차량자태변화급원근변화문제.
As one of the pillar techniques in intelligent transportation system, vehicle tracking has attracted wide attention of many scholars. SIFT (scale-invariant feature transform) features can effectively solve problems such as rotation, scaling, translation of target and provide firm support for tracking vehicle. But traditional SIFT features are unable to distinguish foreground from background, centralism of too many matching features on the background will result in the loss of tracking target. Based on tracking SIFT features, this paper introduces the vehicle tracking method based on SIFT features and GrabCut algorithm. SIFT features can effectively solve problems such as posture change and variance of vehicles in the near to far field, GrabCut algorithm can distinguish foreground from background. The experimental results show that if the camera is not shaking obviously in the day, the initial frame motion detection can realize stable tracking for the moving vehicle and effectively solve problems such as posture change and variance of vehicles in the near to far field.