基于预测的多特征融合Mean-Shift跟踪算法

Multi-feature Fusion Mean-Shift Tracking Algorithm Based on Prediction

视频监控在生活中的应用已经相当广泛,其中视频目标精确跟踪是计算机视觉中应用较广、难度较大的一部分。在实际视频场景中目标存在复杂的变化,如外形变化、部分遮挡、光照变化等,这对Mean-Shift跟踪算法产生了较大的影响。为了解决上述变化导致的跟踪不准确的问题,融合颜色和Gabor-LBP纹理特征进行Mean-Shift跟踪,并利用二次多项式预测运动目标的位置,以提高跟踪的准确度。

The application of video surveillance in life has been quite extensive,especially the main target tracking is widely used in daily life,and it is a difficult part in the computer vision.In the real video scene,there are many complex target appearance changes,such as partial occlusion,light changes,etc.These have a greater impact on the Mean-Shift tracking algorithm.In order to solve the problem of inaccurate tracking caused by the above complex environment,this paper fused the color and Gabor-LBP edge features in Mean-Shift tracking algorithm,and introduced the quadratic polynomial to predict the position of the video target,to improve the tracking accuracy.