VANAHEIM · Video/Audio Networked surveillance system enhAncement through Human-cEntered adaptIve Monitoring

The aim of VANAHEIM is to study innovative surveillance components for autonomous monitoring of complex audio/video surveillance infrastructure, such as the ones prevalent in shopping malls or underground stations. To do so, VANAHEIM addresses three main application-driven research questions: 1.Scene activity modelling algorithms for automatic sensor selection in control room. In everyday practice , surveillance video wall monitors frequently show empty scenes, while there are obviously many cameras looking at scenes in which something (even normal) is happening. Performing a sensor selection at the control room level to autonomously select the streams to display therefore seems required. While this scenario is trivial when dealing with empty vs occupied scenes, building models to characterise the streams content, in terms of usual and unusual activities, turns out to be necessary when dealing with almost all occupied scenes. Furthermore, the need for such selection is even more explicit when dealing with audio streams, for which mosaicing of data is not possible due to the transparent nature of sound. VANAHEIM thus targets the development of such automatic audio/video components allowing to select the audio/video streams to present to operators in control room.2.Investigation of behavioural cues for human-centred monitoring and reporting. VANAHEIM investigates the use of subtle human behavioural cues (head pose, body shape) and social models (e.g. about space occupancy) to perform the live detection of well-defined scenarios of interest. In addition, the project also targets three specific levels of monitoring: 1. individuals, 2. groups of people and 3. crowd/people flow. Last, VANAHEIM targets the development of a situational awareness reporting, which aims at translating the ongoing activities of people into meaningful user-oriented figures, through for example a map-based overlay of the approximate location/number/behaviour of people in the infrastructure.3.Collective behaviour building and online learning from long-term analysis of passenger activities. By combining cognitive science and ethological analysis, VANAHEIM aims at designing models for the identification and characterization of the structures inherent in collective human behaviour. In other words, by continuously analyzing, learning and clustering information about users' locations, routes, activities, interactions with others passengers and/or equipments, and contextual data (time of day, density of people...), VANAHEIM targets a subsystem able to estimate of the long-term trends of large-scale human behaviour, thus allowing the discovery of collective comprehensive daily routines.The evaluation/assessment of the sub-systems developed within the project is covered thanks to the participation of Turin and Paris transport operators; VANAHEIM deployment in both sites will allow to demonstrate the scalability, as well as the performance of the developed system.