Kernel based trackers have been proven to be a promising approach for video object tracking. The use of a single kernel often suffers from occlusion since the available visual information is not sufficient for kernel usage. In order to provide more robust tracking performance, multiple inter-related kernels have thus been utilized for tracking in complicated scenarios. This paper presents an innovative method, which uses projected gradient to facilitate multiple kernels, in finding the best match during tracking under predefined constraints. The adaptive weights are applied to the kernels in order to efficiently compensate the adverse effect introduced by occlusion. An effective scheme is also incorporated to deal with the scale change issue during the object tracking. Moreover, we embed the multiple-kernel tracking into a Kalman filtering-based tracking system to enable fully automatic tracking. Several simulation results have been done to show the robustness of the proposed multiple-kernel tracking and also demonstrate that the overall system can successfully track the video objects under occlusion.

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.

In kernel-based video object tracking, the use of single kernel often suffers from the occlusion. In order to provide more robust tracking performance, multiple inter-related kernels have thus been utilized for tracking in complicated scenarios. This paper presents an innovative method that uses projected gradient to facilitate multiple kernels in finding the best match during tracking under predefined constraints. The adaptive weights are also applied to the kernels in order to efficiently compensate the adverse effect introduced by occlusion. An effective scheme is also incorporated to deal with the scale changing issue during the object tracking. Simulation results demonstrate that the proposed method can successfully track the video object under severe occlusion.

We present an effective real-time approach for automatically estimating 3D human body poses from monocular video sequences. In this approach, human body is automatically detected from video sequence, then image features such as silhouette, edge and color are extracted and integrated to infer 3D human poses by iteratively minimizing the cost function defined between 2D features derived from the projected 3D model and those extracted from video sequence. In addition, 2D locations of head, hands, and feet are tracked to facilitate 3D tracking. When tracking failure happens, the approach can detect and recover from failures quickly. Finally, the efficiency and robustness of the proposed approach is shown in two real applications: human event detection and video gaming.