Performance of optical flow techniques
International Journal of Computer Vision
Object Matching Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Tracking of Position and Velocity With Kalman Snakes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
The watershed transform: definitions, algorithms and parallelization strategies
Fundamenta Informaticae - Special issue on mathematical morphology
Object Tracking Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersnakes: Energy-Driven Watershed Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gradient Vector Flow Fast Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical flow estimation and moving object segmentation based on median radial basis function network
IEEE Transactions on Image Processing
Tracking visible boundary of objects using occlusion adaptive motion snake
IEEE Transactions on Image Processing
Robust optical flow estimation based on a sparse motion trajectory set
IEEE Transactions on Image Processing
Human Tracking by IP PTZ Camera Control in the Context of Video Surveillance
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Fuzzy Feature-Based Upper Body Tracking with IP PTZ Camera Control
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Moving edge segment matching for the detection of moving object
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
A large margin framework for single camera offline tracking with hybrid cues
Computer Vision and Image Understanding
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In this paper, a novel method for accurate subject tracking, by selecting only tracked subject boundary edges in a video stream with a changing background and moving camera, is proposed. This boundary edge selection is achieved in two steps: (1) removing background edges using edge motion, and from the output of the previous step, (2) selecting boundary edges using a normal direction derivative of the tracked contour. Accurate tracking is based on reduction of the effects of irrelevant edges, by only selecting boundary edge pixels. In order to remove background edges using edge motion, the tracked subject motion is computed and edge motions and edges having different motion directions from the subjects are removed. In selecting boundary edges using the normal contour direction, the image gradient values on every edge pixel are computed, and edge pixels with large gradient values are selected. Multi-level Canny edge maps are used to obtain proper details of a scene. Multi-level edge maps allow tracking, even though the tracked object boundary has complex edges, since the detail level of an edge map for the scene can be adjusted. A process of final routing is deployed in order to obtain a detailed contour. The computed contour is improved by checking against a strong Canny edge map and hiring strong Canny edge pixels around the computed contour using Dijkstra's minimum cost routing. The experimental results demonstrate that the proposed tracking approach is robust enough to handle a complex-textured scene in a mobile camera environment.