CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Adaptive-Focus Deformable Model Using Statistical and Geometric Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adjusting Shape Parameters Using Model-Based Optical Flow Residuals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Tracking via Level Set PDEs without Motion Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Coarse-to-Fine Deformable Contour Optimization Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Camouflaged Objects with Weighted Region Consolidation
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of bright targets using wavelets and adaptive thresholding
IEEE Transactions on Image Processing
Level set analysis for leukocyte detection and tracking
IEEE Transactions on Image Processing
Target positioning with dominant feature elements
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
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We propose a method for tracking an object from a video sequence of moving background through the use of the proximate distribution densities of the local regions. The discriminating features of the object are extracted from a small neighborhood of the local region containing the tracked object. The object's location probability is estimated in a Bayesian framework with the prior being the approximated probabilities in the previous frame. The proposed method is both practical and general since a great many of video scenes are included in this category. For the case of less-potent features, however, additional information from such as the motion is further integrated to help improving the estimation of location probabilities of the object. The non-statistical location of an object is then derived through thresholding and shape adjustment, as well as being verified by the prior density of the object. The method is effective and robust to occlusion, illumination change, shape change and partial appearance change of the object.