Active-vision-based multisensor surveillance - an implementation

In this paper, a novel reconfigurable surveillance system that incorporates multiple active-vision sensors is presented. The proposed system has been developed for visual-servoing and other similar applications, such as tracking and state estimation, which require accurate and reliable target surveillance data. In the specific implementation case discussed herein, the position and orientation of a single target are surveyed at predetermined time instants along its unknown trajectory. Dispatching is used to select an optimal subset of dynamic sensors, to be used in a data-fusion process, and maneuver them in response to the motion of the object. The goal is to provide information of increased quality for the task at hand, while ensuring adequate response to future object maneuvers. Our experimental system is composed of a static overhead camera to predict the object's gross motion and four mobile cameras to provide surveillance of a feature on the object (i.e., target). Object motion was simulated by placing it on an xy table and preprogramming a path that is unknown to the surveillance system. The selected cameras are independently and optimally positioned to estimate the target's pose (a circular marker in our case) at the desired time instant. The target data obtained from the cameras, together with their own position and bearing, are fed to a fusion algorithm, where the final assessment of the target's pose is determined. Experiments have shown that the use of dynamic sensors, together with a dispatching algorithm, tangibly improves the performance of a surveillance system.     

An active vision system for multitarget surveillance in dynamic environments.

This paper presents a novel agent-based method for the dynamic coordinated selection and positioning of active-vision cameras for the simultaneous surveillance of multiple objects-of-interest as they travel through a cluttered environment with a-priori unknown trajectories. The proposed system dynamically adjusts not only the orientation but also the position of the cameras in order to maximize the system's performance by avoiding occlusions and acquiring images with preferred viewing angles. Sensor selection and positioning are accomplished through an agent-based approach. The proposed sensing-system reconfiguration strategy has been verified via simulations and implemented on an experimental prototype setup for automated facial recognition. Both simulations and experimental analyses have shown that the use of dynamic sensors along with an effective online dispatching strategy may tangibly improve the surveillance performance of a sensing system.     

Active-Vision for the Autonomous Surveillance of Dynamic,Multi-Object Environments

This paper presents a novel method for active-vision-based sensing-system reconfiguration for the autonomous surveillance of an object-of-interest as it travels through a multi-object dynamic workspace with an a priori unknown trajectory. Several approaches have been previously proposed to address the problem of sensor selection and control. However, these have primarily relied on off-line planning methods and rarely utilized on-line planning to compensate for unexpected variations in a target’s trajectory. The method proposed in this paper, on the other hand, uses a multi-agent system for on-line sensing-system reconfiguration, eliminating the need for any a priori knowledge of the target’s trajectory. Thus, it is robust to unexpected variations in the environment. Simulations and experiments have shown that the use of dynamic sensors with the proposed on-line reconfiguration algorithm can tangibly improve the performance of an active-surveillance system.      

Nanoparticle scanning and detection on flat and structured surfaces using fluorescence microscopy

A new technique is proposed for the scanning and detection of nanoparticles on flat substrates and three-dimensional structures using fluorescence microscopy. This technique is utilized for particle removal measurements especially in semiconductor and hard disk manufacturing. This fluorescent particle scanning technique enables nanoscale particle detection. The technique shows that single particles down to 63 nm could be detected and counted. The technique is also capable of detecting particles in trenches that are as deep as 500 microm.