Constrained multiple kernel tracking for human limbs

In the human body tracking based on video sequences, the pose estimation of the upper/lower limbs is the most challenging task since the limbs possess most variations of motions and are easily occluded. In this work, we present a sophisticated scheme to track the human limbs. First, the tracking is formulated as a constrained optimization problem with multiple kernels. The color features of the upper/lower limbs are used as the control variables in the objective function. Moreover, the inequality constraints are imposed to control the angle between the arm/forearm or upper/lower legs during tracking. Finally, the gradient projection algorithm is adopted to solve the optimization problem with inequality constraints. The proposed scheme is implemented and experimented on HumanEva dataset and self-recorded video sequences including tracking of arm/forearm and upper/lower legs.