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Title: Human Behaviour Understanding for Automotive and Surveillance

Keynote Lecturer: Rita CucchiaraPresented on: 16/01/2018, Funchal, Madeira, Portugal

Abstract: Human behavior understanding (HBU) is a central topic for many different disciplines, from sociology, psychology to, more recently computer science. In this latter framework, Computer Vision and Pattern Recognition are strategic: advancements in motion analysis and interpretation, 2D and 3D video analysis and, obviously, deep learning make computer vision research in HBU a true success story.The talk will focus on HBU for two very related contexts. The first is automotive, a key application area nowadays, due to the fact that cameras are considered mandatory sensing components in cars to support assisted or automatic guidance, to improve the safety and comfort of drivers and passengers. HBU is needed inside and outside the car: to understand what the driver is doing, what he/she can do to drive better and what people around the car are doing. The second is surveillance where the study of human presence and activity has a long tradition. Here the analysis of behaviour of pedestrian, groups of people and crowd is based on computer vision: it now achieves unbelievable results due to the presence of annotated datasets and deep solutions. Surveillance and automotive are becoming always more connected in smart cities environments.In the talk, I will present an overview of recent research centred on humans in video acquired by the city or the car point of view. The talk will discuss the milestones of this research, the improvements and changes in effort due to deep machine learning approaches and the available annotated big data.

Then I will present our research project at Imagelab on persons under surveillance systems and on persons in Human-Vehicle-Interaction applications. These solutions are mostly based on 3D information, saliency analysis and video segmentation and ground on extensive use of deep networks technology. I will discuss deep architectures we propose for understanding person presence, person behaviour and person interaction