LOD Canny Edge Based Boundary Edge Selection for Human Body Tracking

Abstract

We propose a simple method for tracking a nonparameterized subject contour in a single video stream with a moving camera and changing background. Our method is based on level-of-detail (LOD) Canny edge maps and graph-based routing operations on the LOD maps. LOD Canny edge maps are generated by changing scale parameters for a given image. Simple (strong) Canny edge map has the smallest number of edge pixels while the most detailed Canny edge map, Wcanny _N , has the biggest number of edge pixels. We start our basic tracking using strong Canny edges generated from large image intensity gradients of an input image, called Scanny edges to reduce side-effects because of irrelevant edges. Starting from Scanny edges, we get more edge pixels ranging from simple Canny edge maps until the most detailed Canny edge maps. LOD Canny edge pixels become nodes in routing, and LOD values of adjacent edge pixels determine routing costs between the nodes. We find a best route to follow Canny edge pixels favoring stronger Canny edge pixels. Our accurate tracking is based on reducing effects from irrelevant edges by selecting the stronger edge pixels, thereby relying on the current frame edge pixel as much as possible contrary to other approaches of always combining the previous contour. Our experimental results show that this tracking approach is robust enough to handle a complex-textured scene.