A robust real-time tracking system based on an adaptive selection mechanism for mobile robots

Extensive research has been conducted in the domain of object tracking. Among the existing tracking methods, most of them mainly focus on using various cues such as color, texture, contour, features, motion as well as depth information to achieve a robust tracking performance. The tracking methods themselves are highly emphasized while properties of the objects to be tracked are usually not exploited enough. In this paper, we first propose a novel adaptive tracking selection mechanism dependent on the properties of the objects. The system will automatically choose the optimal tracking algorithm after examining the textureness of the object. In addition, we propose a robust tracking algorithm for uniform objects based on color information which can cope with real world constraints. In the mean time, we deployed a textured object tracking algorithm which combines the Lucas-Kanade tracker and a model based tracker using the Random Forests classifier. The whole system was tested and the experimental results on a variety of objects show the effectiveness of the adaptive tracking selection mechanism. Moreover, the promising tracking performance shows the robustness of the proposed tracking algorithm. The computation cost of the algorithm is very low, which proves that it can be further used in various real-time robotics applications.