An Optical Flow Based Multi-Object Tracking Approach Using Sequential Convex Programming

Object tracking, as one of the open topics in the past few decades, has been widely applied in the field of video processing. Although there has been intensive research on this topic, some challenges still exist, such as occlusions, clutter and dynamic scenarios. In this paper, we propose a new multi-layer formulation for multi-object tracking, which incorporates the traditional optical flow constraints with a new product term. Then, a sequential convex programming (SCP) based method is presented to solve the resulting non-convex optimization problem. The proposed method can achieve a linear convergence rate, which is demonstrated by our numerical results. In order to illustrate the performance of our approach, it is implemented for composite videos, where two objects move in opposite directions. Different experimental settings based on noise-free, occlusions, clutter and dynamic scenarios show that the method can robustly recover multiple layers and the velocity of the objects. Moreover, the experiments indicate considerable potential for handling more complex scenarios.