Vision offers a powerful sensing modality to understand and interact with the physical world. The rapid progress in hardware, models and algorithms is supporting the emergence of applications for the recognition of events from wearable smart cameras and camera-equipped robots, such as unmanned land and aerial vehicles (i.e. self-driving cars and mini-drones).
In this context I will present an online multi-target tracker that exploits both high- and low-confidence target detections in a Probability Hypothesis Density Particle Filter framework to continuously localise people from moving cameras. High-confidence detections are used for label propagation and target initialization, whereas low-confidence detections only support the propagation of labels. Data association is performed after prediction to avoid computationally expensive labelling procedures such as clustering. I will discuss results on the Multiple Object Tracking benchmark dataset and present several application scenarios.
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 is 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 over 200 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).
http://www.di.univr.it/?ent=seminario&id=3983&lang=itContact person: Gloria Menegaz
