Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems
International Journal of Computer Vision
Computer Vision and Image Understanding
Variance reduction techniques in particle-based visual contour tracking
Pattern Recognition
Motion-based enhancement of optical imaging
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Mice and larvae tracking using a particle filter with an auto-adjustable observation model
Pattern Recognition Letters
A hierarchical feature fusion framework for adaptive visual tracking
Image and Vision Computing
Shape based appearance model for kernel tracking
Image and Vision Computing
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We present a particle filtering algorithm for robustly tracking the contours of multiple deformable objects through severe occlusions. Our algorithm combines a multiple blob tracker with a contour tracker in a manner that keeps the required number of samples small. This is a natural combination because both algorithms have complementary strengths. The multiple blob tracker uses a natural multitarget model and searches a smaller and simpler space. On the other hand, contour tracking gives more fine-tuned results and relies on cues that are available during severe occlusions. Our choice of combination of these two algorithms accentuates the advantages of each. We demonstrate good performance on challenging video of three identical mice that contains multiple instances of severe occlusion.