IAPR Distinguished Speaker: Professor Brian Lovell
Director of the Advanced Surveillance Group in the School of ITEE, UQ
"Transcontinental Surveillance and Non-Cooperative CCTV Face Recognition"
Abstract: In this keynote I will describe our biometrics and surveillance research work on a huge surveillance project currently running with face recognition appliance nodes in Australia, UK, and Brazil. This fully operational system runs securely over the internet with edge processing to massively reduce bandwidth requirements and improve privacy. There is absolutely no need for a dedicated fibre network to connect all the high speed cameras - indeed wireless and mobile connectivity is a viable option. We have developed advanced low-resolution video face recognition technologies which work extremely effectively and affordably in this unique architecture. The talk will describe this developing transcontinental surveillance system and how it is designed to be fully scalable to both national and international operation. A single cloud-based incident management backbone is accessible to all users from anywhere in the world. Along the way we will discuss the basics of robust CCTV -based video face recognition and the huge technical challenges of simultaneous pose, expression, illumination, obscuration, and motion blur compensation. We will also discuss very recent work on robust face detection, land marking, and tracking to enable our systems to work on a crowd of people walking quickly past the cameras. The systems also perform cross-camera matching to measure queue lengths and estimates the gender and age of customers for retail applications. There will be live demonstrations of our systems in real-time operation including both mobile and wearable face recognition apps.
Biography: Brian C. Lovell was born in Brisbane, Australia in 1960. He received the BE in electrical engineering in 1982, the BSc in computer science in 1983, and the PhD in signal processing in 1991: all from the University of Queensland (UQ). Professor Lovell is Director of the Advanced Surveillance Group in the School of ITEE, UQ. He was President of the International Association for Pattern Recognition (IAPR) [2008-2010], and is Fellow of the IAPR, Senior Member of the IEEE, and voting member for Australia on the Governing Board of the IAPR. He is Program Co-Chair of the International Conference of Pattern Recognition (ICPR2016) in Cancún Mexico, and was General Co-Chair of the IEEE International Conference on Image Processing in Melbourne, 2013 and Program Co-Chair of ICPR2008 in Tampa, Florida. His interests include non-cooperative Face Recognition, robust face detection, Biometrics, and Pattern Recognition. His work in biometrics and surveillance has won numerous international awards including the prestigious Best CCTV System at IFSEC2011, Birmingham, for Face in the Crowd recognition. He also won the government sponsored Asia Pacific ICT Trophy for Best R&D in the Asia Pacific region in Phuket, Thailand in 2011.
Associate Professor Marcus Frean
School of Engineering and Computer Science, VUW
"Detecting Astronomical Oddities"
Abstract: We address the question of finding "anomalous" regions of pixels in any image, with a view to source-finding in astronomical images. The next generation of radio telescopes will generate exabytes of data on hundreds of millions of objects, making automated methods for the detection of astronomical objects ("sources") essential. Of particular importance are faint, diffuse objects embedded in considerable background noise. It would be nice to have a method that could automatically identify these sources and involves very little manual tuning, yet remains tractable to perform on SKA-sized imagery. I will describe such an algorithm, capable of detecting diffuse, dim sources of any size in an astronomical image. These sources often defeat traditional methods for source finding, which expand regions around points of high intensity. Extended sources often have no bright points and are only detectable when viewed as a whole, so a more sophisticated approach is required. Our algorithm operates at all scales simultaneously by considering a tree of nested candidate bounding boxes, and inverts a hierarchical Bayesian generative model to obtain the probability of sources existing at given locations and sizes. This model naturally accommodates the detection of nested sources, and no prior knowledge of the distribution of a source, or even the background, is required. The algorithm scales nearly linear with the number of pixels making it feasible to run on large images, and requires minimal parameter tweaking to be effective. We demonstrate the algorithm on several types of astronomical and artificial images.
Biography: Marcus is an Associate Professor in the School of Engineering and Computer Science at Victoria University of Wellington. He is also a Principal Investigator with Te Punaha Matatini, and with the Science for Technological Innovation (National Science Challenge), and a member of the team at Victoria involved in the design phase of the Science Data Processor for the SKA. His research interests are in machine learning and complex systems.
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