A distributed camera system for multi-resolution surveillance

We describe an architecture for a multi-camera, multi-resolution surveillance system. The aim is to support a set of distributed static and pan-tilt-zoom (PTZ) cameras and visual tracking algorithms, together with a central supervisor unit. Each camera (and possibly pan-tilt device) has a dedicated process and processor. Asynchronous interprocess communications and archiving of data are achieved in a simple and effective way via a central repository, implemented using an SQL database. Visual tracking data from static views are stored dynamically into tables in the database via client calls to the SQL server. A supervisor process running on the SQL server determines if active zoom cameras should be dispatched to observe a particular target, and this message is effected via writing demands into another database table. We show results from a real implementation of the system comprising one static camera overviewing the environment under consideration and a PTZ camera operating under closed-loop velocity control, which uses a fast and robust level-set-based region tracker. Experiments demonstrate the effectiveness of our approach and its feasibility to multi-camera systems for intelligent surveillance

Multiple Views Tracking of Maritime Targets

This paper explores techniques for multiple views target tracking in a maritime environment using a mobile surveillance platform. We utilise an omnidirectional camera to capture full spherical video and use an Inertial Measurement Unit (IMU) to estimate the platform?s ego-motion. For each target a part of the omnidirectional video is extracted, forming a corresponding set of virtual cameras. Each target is then tracked using a dynamic template matching method and particle filtering. Its predictions are then used to continuously adjust the orientations of the virtual cameras, keeping a lock on the targets. We demonstrate the performance of the application in several real-world maritime settings

Drones for Disaster Response and Relief Operations

Aerial drones are one of the most promising and powerful new technologies to improve disaster response and relief operations. Drones naturally complement traditional manned relief operations by helping to ensure that operations can be conducted safer, faster, and more efficiently. When a disaster occurs, drones may be used to provide relief workers with better situational awareness, locate survivors amidst the rubble, perform structural analysis of damaged infrastructure, deliver needed supplies and equipment, evacuate casualties, and help extinguish fires -- among many other potential applications. This report will discuss how drones and the aerial data they collect can be used before, during, and after a disaster. It includes an overview of potential solutions and deployment models, as well as, recommendations on removing regulatory barriers to their use. The American Red Cross, leading private sector companies, and federal agencies coordinated by Measure, a 32 Advisors Company, have come together to explore and explain how and why drones should be used in the wake of natural disasters and other emergencies that threaten widespread loss of life and property

Interactive Learning of Probabilistic Decision Making by Service Robots with Multiple Skill Domains

This thesis makes a contribution to autonomous service robots, centered around two aspects. The first is modeling decision making in the face of incomplete information on top of diverse basic skills of a service robot. Second, based on such a model, it is investigated, how to transfer complex decision-making knowledge into the system. Interactive learning, naturally from both demonstrations of human teachers and in interaction with objects, yields decision-making models applicable by the robot

Collaborative Tracking of Objects in EPTZ Cameras

This paper addresses the issue of multi-source collaborative object tracking in high-definition (HD) video sequences. Specifically, we propose a new joint tracking paradigm for the multiple stream electronic pan-tilt-zoom (EPTZ) cameras. These cameras are capable of transmitting a low resolution thumbnail (LRT) image of the whole field of view as well as a high-resolution cropped (HRC) image for the target region. We exploit this functionality to perform joint tracking in both low resolution image of the whole field of view as well as high resolution image of the moving target. Our system detects objects of interest in the LRT image by background subtraction and tracks them using iterative coupled refinement in both LRT and HRC images. We compared the performance of our joint tracking system with that of tracking only in the HD mode. The results of our experiments show improved performance in terms of higher frame rates and better localization. Keywords: Collaborative tracking, EPTZ tracking, Mean-shift analysis 1