Robust Tracking of Articulated Human Movements Through Component-Based Multiple Instance Learning with Particle Filtering

Abstract

We present a robust approach for tracking human subjects as their limbs and torso are engaged in large articulated movements while the entire body is executing a large translational motion with respect to the pointing angle of the camera. While the articulated movements can be handled by the recently proposed Component-Based Multiple Instance Learning (CMIL) tracker, the large translational motions by the target require that we also use a motion prediction framework to more accurately estimate the most probable positions of the target in the next frame of a video sequence. In the work we report here, this prediction is carried out with a particle filter. This coupling between CMIL based tracking and particle filtering yields a much more accurate estimate of candidate positions of the target in the next frame given the position of the target in the current frame. We validate this new approach by demonstrating results on videos of human subjects that are simultaneously executing large articulated movements with their limbs and torso while the subjects themselves are in some translational motions with respect to the pointing angle of the camera.