基于光流法的运动目标检测与跟踪算法

选用Harris角点作为跟踪对象,将尺度空间引入角点检测,提取特征尺度上的Harris角点,并进行曲率非极大值抑制,滤除"伪角点",提高角点检测对尺度变化的抗扰能力.跟踪算法选用结合图像金字塔的光流法,迭代计算光流,并提出基于光流误差的跟踪算法,即用不同时间流的运动轨迹在同一帧图像的误差来衡量运动跟踪情况,避免跟踪点因被遮挡、消失或者纹理特征发生变化而导致跟踪失败.通过对不同视频图像进行检测的结果证明基于改进的角点提取和图像金字塔的光流法具有良好的跟踪效果,引入光流误差可以有效地滤除跟踪失败的特征点,准确估计运动目标的位置.

Object Detecting and Tracking Algorithm Based on Optical Flow

Harris corner points were adopted as tracking objects， and scale space was introduced into corner point detection in order to extract Harris corner points in feature scale. Then curvature was computed to filter out false corners and enhance adaptability to scale change. Optical flow method was adopted for the tracking algorithm based on image pyramid， in which the optical flow iteratively was computed. And the tracking algorithm based on the optical flow error was proposed. That is， the trajectory error in the same frame with different time flow was used to evaluate the tracking situation. In this way， tracking failure was avoided when the tracking object is hidden， disappears or textural features change. Experimental results of different video sequences show that the proposed optical flow tracking algorithm based on improved corner extraction and image pyramid has better tracking performances. The feature points could be filtered effectively that lead to tracking failure with the introduction of optical flow error method， and the object positions are estimated accurately.