Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A fast level set method for propagating interfaces
Journal of Computational Physics
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
Region Tracking via Level Set PDEs without Motion Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Traditional contour tracking methods can not handle abrupt motion or low frame rate video. This is because the basis of the traditional tracking lies in the assumption that the motion is smooth between consecutive frames. However, the abrupt motion destroys the foundation of this assumption. In this paper, we integrate the stochastic search into the level set evolution to reinstitute the continuity. Our approach can be viewed as a two-layer hierarchical level set-based tracking framework in which Particle Swarm Optimization (PSO) and level set evolution are fused seamlessly. In the first layer, the PSO is adopted to capture the global motion of the object. The coarse contour is obtained by applying the global motion to the contour in the previous frame. For the second layer, the level set evolution based on the coarse contour is carried out to track the local deformation, which results in the actual contour. The promising experimental results for numerous real videos reveal the effectiveness of our approach.