Countless applications will benefit from the capabilities offered by networks of wireless cameras that can autonomously sense, compute, decide and communicate. These networks are composed of cameras whose algorithms need to adapt in response to unknown or dynamic environments and to changes in the assigned task. In this seminar I will present recent methods for cameras to move and to interact locally to track targets and reach coordinated decisions under resource and physical constraints. I will discuss how cameras self-evaluate their performance and improve the quality of the task they are executing through collaboration, adaptively. Applications benefitting from this revolution include self-driving vehicles, multi-robot systems, wide-area surveillance, disaster management and various Internet of Things applications for smart cities and smart homes.
Andrea Cavallaro is Professor of Multimedia Signal Processing and Director of the Centre for Intelligent Sensing at Queen Mary University of London, UK. He received his Ph.D. in Electrical Engineering from the Swiss Federal Institute of Technology (EPFL), Lausanne, in 2002. He was a Research Fellow with British Telecommunications (BT) in 2004/2005 and was awarded the Royal
Academy of Engineering teaching Prize in 2007; three student paper awards on target tracking and perceptually sensitive coding at IEEE ICASSP in 2005, 2007 and 2009; and the best paper award at IEEE AVSS 2009.
Prof. Cavallaro Senior Area Editor for the IEEE Transactions on Image Processing; and Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology and IEEE Multimedia. He has published more than 150 journal and conference papers, one monograph on Video tracking (2011, Wiley) and three edited books: Multi-camera networks (2009, Elsevier); Analysis, retrieval and delivery of multimedia content (2012, Springer); and Intelligent multimedia surveillance (2013, Springer).