关键词: 车辆跟踪, 尺度不变特征转换（SIFT）, GrabCut

Abstract: 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.

Key words: vehicle tracking, scale-invariant feature transform (SIFT), GrabCut