  Feature tracking is a fundamental problem in computer vision, with
applications in many computer vision tasks, such as visual SLAM and action
recognition. This paper introduces a novel multi-body feature tracker that
exploits a multi-body rigidity assumption to improve tracking robustness under
a general perspective camera model. A conventional approach to addressing this
problem would consist of alternating between solving two subtasks: motion
segmentation and feature tracking under rigidity constraints for each segment.
This approach, however, requires knowing the number of motions, as well as
assigning points to motion groups, which is typically sensitive to the motion
estimates. By contrast, here, we introduce a segmentation-free solution to
multi-body feature tracking that bypasses the motion assignment step and
reduces to solving a series of subproblems with closed-form solutions. Our
experiments demonstrate the benefits of our approach in terms of tracking
accuracy and robustness to noise.
