A Radar-Enabled Collaborative Sensor Networking Integrating COTS Technology for Surveillance and Tracking

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (\u3c$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios

Vehicle detection and tracking using wireless sensors and video cameras

This thesis presents the development of a surveillance testbed using wireless sensors and video cameras for vehicle detection and tracking. The experimental study includes testbed design and discusses some of the implementation issues in using wireless sensors and video cameras for a practical application. A group of sensor devices equipped with light sensors are used to detect and localize the position of moving vehicle. Background subtraction method is used to detect the moving vehicle from the video sequences. Vehicle centroid is calculated in each frame. A non-linear minimization method is used to estimate the perspective transformation which project 3D points to 2D image points. Vehicle location estimates from three cameras are fused to form a single trajectory representing the vehicle motion. Experimental results using both sensors and cameras are presented. Average error between vehicle location estimates from the cameras and the wireless sensors is around 0.5ft

Autonomous Grasping Using Novel Distance Estimator

This paper introduces a novel distance estimator using monocular vision for autonomous underwater grasping. The presented method is also applicable to topside grasping operations. The estimator is developed for robot manipulators with a monocular camera placed near the gripper. The fact that the camera is attached near the gripper makes it possible to design a method for capturing images from different positions, as the relative position change can be measured. The presented system can estimate relative distance to an object of unknown size with good precision. The manipulator applied in the presented work is the SeaArm-2, a fully electric underwater small modular manipulator. The manipulator is unique in its integrated monocular camera in the end-effector module, and its design facilitates the use of different end-effector tools. The camera is used for supervision, object detection, and tracking. The distance estimator was validated in a laboratory setting through autonomous grasping experiments. The manipulator was able to search for and find, estimate the relative distance of, grasp, and retrieve the relevant object in 12 out of 12 trials.publishedVersio