Tracking Multiple Mouse Contours (without Too Many Samples)

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.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Kristin Branson, Serge Belongie
• Keywords: Computer Vision, Contour Tracker, CVPR 2005, Multiple Blob Tracker, Multiple Deformable Objects, Particle Filtering Algorithm, Severe Occlusions