Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Pfinder: Real-Time Tracking of the Human Body
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
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
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Integrating intensity and texture differences for robust change detection
IEEE Transactions on Image Processing
Journal of Mathematical Imaging and Vision
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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In this paper, a novel approach is proposed for tracking non-rigid moving objects with a stationary camera under varying illumination conditions. By using the well-known Bayesian framework, our method combines a specially designed color model and a texture model through a level set partial differential function. Different from traditional methods, our color and texture models can extract robust information that is insensitive to illumination variations. This makes it feasible to determine whether temporal variations in images are caused by object motion or illumination changes. Moving objects can then be tracked robustly and accurately in spite of abrupt illumination variations. Since no prior shape information about moving objects is required, it is especially suitable for the situation where shape information is hard to be obtained. Experiments show that this method has a great capability to track non-rigid moving objects under globally or locally varying illumination conditions, even when light intensities change abruptly.