This paper presents a seemingly novel approach to accomplishing mobile robot localization and navigation in a large unknown workspace with a set of vision sensors based on an effective on-line calibration strategy. Each adjacent IP camera pair for visual sensing is assumed to have an overlapping field of view, but the positions as well as orientations with respect to either the Cartesian space or the mobile robot are unknown. The idea is to control the mobile robot to actively perform calibration with any stationary IP camera that can observe the five preselected color-coded features onboard the mobile robot, and thus establish coordinate transformations among these IP cameras. In particular, the coordinate transformation from the mobile robot to the IP camera is recursived updated based on all observed data and the kinematic model of the mobile robot. The proposed system only requires a PC and a network of stationary IP cameras without performing off-line calibration. Therefore, the proposed approach appears to be low-cost, effective, efficient, and flexible.

Abstract As the prevalence of Wireless Sensor Networks (WSNs) grows in the military and civil domains, network security becomes an evermore critical concern. In Sybil attack, malicious nodes forge a lot of fake identities to disrupt the network's protocols. Previous work used either authentication‐based mechanisms which use shared encryption keys, or location information to verify the identities of the nodes. By contrast, this paper uses only neighboring node relation to verify the node identities. The evaluation results show that the detection rate was around 95% when the network was not very sparse. Copyright © 2010 John Wiley & Sons, Ltd.

This paper presents a real-time vision-based vehicle detection system employing an online boosting algorithm. It is an online AdaBoost approach for a cascade of strong classifiers instead of a single strong classifier. Most existing cascades of classifiers must be trained offline and cannot effectively be updated when online tuning is required. The idea is to develop a cascade of strong classifiers for vehicle detection that is capable of being online trained in response to changing traffic environments. To make the online algorithm tractable, the proposed system must efficiently tune parameters based on incoming images and up-to-date performance of each weak classifier. The proposed online boosting method can improve system adaptability and accuracy to deal with novel types of vehicles and unfamiliar environments, whereas existing offline methods rely much more on extensive training processes to reach comparable results and cannot further be updated online. Our approach has been successfully validated in real traffic environments by performing experiments with an onboard charge-coupled-device camera in a roadway vehicle.

This paper presents a real-time vision-based side vehicle detection system employing a parts-based boosting algorithm. Working at the part level, as opposed to the whole object level, enables a more flexible class representation and allows scenes in which the query object is significantly occluded to be classified. Therefore, a parts-based learning approach is proposed in order to better deal with side vehicle variability, illumination conditions, partial occlusions, and rotations. Most existing boosting learning algorithms usually select weak classifiers by minimizing a cost directly associated with the error rate, where the learned strong classifier may be sub-optimal for applications in terms of error rate. Nevertheless, the proposed Adaboost approach selects weak classifiers by minimizing multiple types of error functions. The idea is to define multiple types of error functions based on current strong classifier and each selected weak classifier results to represent different effects of each weak classifier. Therefore, weak classifiers can be selected with different requirements at the same time to avoid a sub-optimal solution. To reduce system computation, window-based tracking is employed. Moreover, Kalman filtering is used to predict the position of each part of vehicles in the image plane to effectively relocate the tracking windows. Compared with existing approaches, the proposed system appears to be capable of improving system efficiency and accuracy under varying lighting conditions, changing vehicle poses, and in the presence of partial occlusions. Our approach has been successfully validated in real traffic environments by performing experiments with a CCD camera mounted onboard a highway vehicle.

Due to the fact that existing surveillance systems usually employ fixed or automatically-patrolled cameras, they cannot effectively track invaders in real-time. Therefore, an automatic head tracking system in indoor cluttered environment is proposed. This system consists of two parts, automatic head tracking system and remote monitoring and control system. In the automatic head tracking system, the position of human head on image plane can be determined based on head tracking strategy. According to the position and size of human head on image plane, a suitable fuzzy rule is designed to maintain the position of human head near image center with appropriate size by controlling the active camera via pan, tilt, and zoom. Specifically, the position of the moving object on image plane can be determined using moving object detection method. Then a sizable tracking window to track human head is used to reduce redundant computation. Moreover, color recognition, template matching, and head contour matching methods are integrated for tracking human head robustly. In order to achieve better performance, Kalman filtering is employed to predict the position of human head on image plane. The proposed active head tracking system has been successfully validated in indoor cluttered environment.

The paper deals with hybrid control of a planar robot employing Cartesian-based force and visual servoing. The proposed robotic system is capable of autonomously controlling the pose of an instrument mounted on the end-effector of a planar robotic manipulator using force and visual data. The goal of the autonomous task is to drive the instrument to a visually determined planar contour and continue tracking the contour in desired pose and contact force with precision. The proposed control architecture is suitable for applications that require simultaneous force and pose control in unknown environments. Our approach has been successfully validated in a real task environment by performing experiments with an industrial robotic manipulator equipped with force and visual sensors.

This article addresses the visual servoing of a rigid robotic manipulator under fixed camera configuration. A laser pointer is mounted on the end-effector of a robot whose orientation can be controlled by automatic visual feedback. The control goal is to drive the laser spot to follow a visually determined planar contour by using a single-camera vision system. In this research, image-based control approach is proposed to achieve "precise" positioning in the absence of measurement noise. With this system, one can achieve automatic planar contour following using approximately calibrated single-camera vision. The control strategy is successfully validated in a real-time PC-based experimental system by performing experiments on arbitrary contour following.

This article addresses the visual servoing of a rigid robotic manipulator under fixed camera configuration. The control goal is to move the robot end-effector to a visually determined target position by using a vision system equipped with two fixed cameras. A general image-based controller is proposed to achieve "precise" positioning in the absence of measurement noise. Based on the "epipolar constraint", we have shown that the overall closed-loop system using redundant image features, is exponentially stable provided that the camera model imprecision is small.

This article describes the theory and implementation of a system that positions a robot manipulator using visual information from two cameras. The system simultaneously tracks the robot end-effector and visual features used to define goal positions. An error signal based on the visual distance between the end-effector and the target as defined and a control law that moves the robot to drive this error to zero is derived. The control law has been integrated into a system that performs tracking and stereo control on a single processor with no special purpose hardware at real-time rates. Experiments with the system have shown that the controller is so robust to calibration error that the cameras can be moved several centimeters and rotated several degrees while the system is running with no adverse effects.>