In a video-based surveillance system, a mobile camera can provide dynamical and wider monitoring range and the video data transmitted from cooperative mobile cameras can be used to actively detect the objects of interest. However, it is a difficult task to accurately detect the moving objects from the image frames captured by the mobile cameras and the data flow of surveillance video from multiple cameras could be huge. The camera motion usually causes the shifting of static background as well as the moving objects in the captured image frames. In order to correctly estimate the motion of moving objects, a voting-based motion estimation algorithm is proposed to process the image frames captured by the mobile camera. Based on the estimation, a content-based video transmission mechanism is then implemented to further effectively decrease encoding cost and bandwidth utilization. The overall approach consists of voting-based motion estimation, moving object edges detection and content-based sampling coding at temporal and spatial scales. Without knowing the prior knowledge of camera motion, the motion estimation algorithm only utilizes the shifting information of edges of static background to estimate the camera movement. The shifting information is determined based on the voting decision of several representative regions of interest and the estimated motion is then used to compensate for the visual content obtained from the captured image frames. The proposed algorithms have been experimentally tested on several practical scenarios and it is demonstrated that, under limited network bandwidth, the transmitted image quality can be progressively achieved and the transmission bandwidth utilization can be effectively decreased.

In this paper we present the development of a visual servo controller to laterally navigate a mobile vehicle tracking the lane marker on a ground path automatically. Based on linear approximation of the lane marker and kinematics of the vehicle, we first derive the equations of motion for the system of lateral navigation. Then, utilizing the feedback linearization technique, we design the visual servo controller and, through a stability analysis, we can guarantee the tracking errors converge to zero exponentially. In the implementation, we adopt an efficient lane-marker detection system to measure the position and orientation of the lane marker relative to a look-ahead position of the vehicle. Finally, several experimental results are provided to validate the control performance.

This paper presents the development of a visual tracking and visual servo system for a reconnoitering air vehicle. The air vehicle is mounted with a camera platform underneath. It is proposed to perform an aerial reconnoitering mission for continuously viewing an interesting target. The vision system is composed of a hybrid visual tracking system and an image based fuzzy logic visual servo system. The designed hybrid visual tracking system can track arbitrary-shaped moving object. Also, the followed visual servo system is to control the air vehicle hovering the target autonomously. The overall system is simulated and experimented in a virtual reality environment to validate the research work.

The problem of adaptive coordinated control of multiple robot arms transporting an object is addressed. A stable adaptive control scheme for both trajectory tracking and internal force control is presented. Detailed analyses on tracking properties of the object position and velocity and the internal forces exerted on the object are given. It is shown that this control scheme achieves satisfactory tracking performance without using the measurement of contact forces and their derivatives. It can be shown that this scheme can be realized by decentralized implementation to reduce the computational burden. Moreover, some efficient adaptive control strategies can be incorporated to reduce the computational complexity.>

The authors address the problem of adaptive hybrid controller design for constrained robots, with consideration of computational efficiency. Two efficient control schemes based on Lagrange-Euler and Newton-Euler dynamics formulations are presented. Detailed analyses of tracking properties of joint positions, velocities, and constrained forces are derived for both the Lagrange-Euler approach and the Newton-Euler approach. Although control laws in these two approaches are developed independently, a close connection between them is indicated, which suggests a possible bridge over different general adaptive approaches based on the two dynamics formulations.>

An adaptive force control, scheme is presented that enables a single-link mechanism with joint flexibility to track a desired force trajectory. A dynamic model of the link system is derived, based on which a two-stage controller is constructed. It is shown that although all the system parameters including environment stiffness are unknown except for some of their bounds, all signals inside the closed-loop system remain uniformly bounded. Moreover, the force tracking error is driven to zero asymptotically. Simulation examples are provided to demonstrate the effectiveness of the proposed controller.>