A new algorithm to rigid and non-rigid object tracking in complex environments

With a focus on complex environments, the present paper describes a new algorithm in rigid and non-rigid object tracking through color feature. Object tracking in these environments is taken into consideration as real-time applications, such as manufacturing, surveillance and monitoring, smart rooms, and so on, where partial or full occlusion sensibly occurs. As is obvious, the best color-based object tracking algorithm is now known, as the mean shift (MS) iterative procedure, to find the location of an object in image sequences. The algorithm performance is not unfortunately acceptable once objects in complex environments need to be tracked. In fact, the main aim of the present research is to improve the MS tracking algorithm, by proposing an improved convex kernel function, which is now realized in association with the Kalman filter approach (KFA). In the algorithm proposed here, the KFA is employed to solve the full occlusion problems since the speed for the objects is constant. Subsequently, the present investigated robust kernel function has been designed to dominate the low saturation and partial occlusion problems.