Target Tracking Control Based on Dual Model Fusion Recent years have witnessed rapid progress in target tracking. To track a moving target for mobile robots, however, both performance and speed of the algorithm are indispensable. This paper proposes a dual model fusion strategy to improve target tracking drift. Among them, the spatiotemporal context model in middlelevel feature space (MFSTC) is utilized to ameliorate target tracking effect when illumination or appearance changes, the mean shift based on 3D back projection (MS3D) is fused to allow the algorithm to tackle occlusion and deformation. Tracking controller based on visual servo is also designed for mobile robots. We validate the efficiency of the proposed fusion model on the university of Birmingham RGBD Tracking Benchmark (BTB) and show that our approach compares favorably with the stateofthearts, mobile robots using our approach can track targets robustly under various challenging scenes.

A Twolevel Adaptive Target Recognition and Tracking Method Based on Vision for Multirobot System The visionbased target recognition and tracking have received much attention in the field of robotics. Existing methods mainly focus on the vision perception of individual robot with a single view, however, the performance is susceptible to illumination and occlusion. Multirobot collaborative perception provides a potential solution to deal with the limitation of singleview observation, however, the challenging of environmental adaptability for multirobot collaborative decision still remains unsolved. To solve this problem, this paper proposes a twolevel adaptive target recognition and tracking method based on vision for multirobot system. The problem of multirobot target recognition and tracking is solved under a twolevel framework, which contains the features fusion level of individual robot and the cooperation level of multirobot system. In the first level, the features measuring results that influence the visual perception of individual robot are fused, while the second level combines the voting of each robot together to determine the target for multirobot system. Both the features measuring weights and robots voting weights are adaptively updated according to their evaluation, which lead to a beneficial result where the features and robots with higher accuracy play major roles in the first and second levels, respectively. Therefore, a good adaptability to the environments can be guaranteed. The experimental results show that the proposed approach can realize the coordination of multirobot system in target recognition and tracking with an effective performance.