摘要
提出了一种基于光流跟踪的室外场景下的阴影边缘检测与跟踪框架。利用从已知结果中提取的边缘信息特征训练SVM,对前后两帧进行光流跟踪,利用SVM模型从不稳定跟踪点的邻域Canny置信边缘中识别符合阴影特征的点。针对视点移动带来的场景新材质问题,设计了动态更新SVM的方法。阴影投射区域的复杂性可能使得SVM失效,针对这种问题,使用了区域对比算法来提高结果的准确性。实验结果表明,算法可以准确的检测和跟踪移动视点下视频中移动物体如运动人体的投射阴影。
A shadow edge detection and tracking framework based on optical flow tracking in outdoor scenes is proposed. The SVM is trained by using the edge information features extracted from the known results; the optical flow tracking is performed on the two frames before and after; and the SVM model is used to identify the points corresponding to the shadow features from the neighborhood Canny confidence edges of the unstable tracking points. A method for dynamically updating the SVM is designed for the new material problem of the scene caused by the viewpoint movement. The complexity of the shadow projection area may invalidate the SVM. For this problem, a region comparison algorithm is used to improve the accuracy of the results. The experimental results show that the algorithm can accurately detect and track the projected shadows of moving objects such as moving humans in the video under the mobile view.
引文
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