Visual Sensor Networks (VSNs) exploit the processing and communication capabilities of modern smart cameras to handle a variety of applications such as security and surveillance and critical infrastructure protection. The performance of various tasks in such applications, such as activity recognition, tracking, etc., can be severely affected by the detection module especially when considering low-cost embedded smart cameras with limited processing capabilities. Hence, this paper presents research towards the development of optimization algorithms and decision making solutions to improve the detection performance of such VSNs. Specifically, it introduces a probabilistic detection model that can be used to characterize the detection capabilities of cameras, and shows how it can be used to reconfigure VSNs. Experimental as well as simulation results indicate that the proposed solution is able to effectively improve the robustness and overall detection performance of VSNs.
['Christos Kyrkou', 'Stelios Timotheou', 'Eftychios G. Christoforou', 'Theocharis Theocharides', 'Christos G. Panayiotou', 'Marios M. Polycarpou']
Towards Improving the Detection Performance in Collaborative Visual Sensor Networks