一种基于Mean Shift和Kalman预测的带宽自适应跟踪算法

Bandwidth-adaptive tracking algorithm based on Mean Shift and Kalman prediction

Mean Shift算法是视觉监控领域广泛应用的经典目标跟踪方法,但对于速度过快或尺度变化大的目标的跟踪存在较大的缺陷。针对这一问题,提出了一种基于Mean Shift和Kalman方法预测的带宽自适应跟踪算法。该算法提出以Kalman预测目标在下帧中的位置作为Mean Shift迭代初始位置,以高效锁定各类运动目标;同时采用增量试探法自动调节带宽以适应目标的尺度变化。通过对行人和车辆等不同监控对象的实验表明,该跟踪算法具有良好的鲁棒性。

As a widely used traditional tracking technique in visual surveillance, Mean Shift algorithm has a deficiency in handling moving targets with high speed or large scale change. In order to sove this problem, a bandwidth-adaptive tracking algorithm based on Mean Shift and Kalman prediction was proposed. The algorithm uses Kalman filter to predict the positions of fast moving objects in the successive frame, which are as the initial positions for Mean Shift iteration. Bandwidth trials is utilized to adjust the bandwidth automatically for targets＇ scale change. The experimental results of pedestrians and vehicle tracking show that our algorithm is effective and robust.