Experimental Study of Collective Pedestrian Dynamics

We report on two series of experiments, conducted in the frame of two different collaborations designed to study how pedestrians adapt their trajectories and velocities in groups or crowds. Strong emphasis is put on the motivations for the chosen protocols and the experimental implementation. The first series deals with pattern formation, interactions between pedestrians, and decision-making in pedestrian groups at low to medium densities. In particular, we show how pedestrians adapt their headways in single-file motion depending on the (prescribed) leader’s velocity. The second series of experiments focuses on static crowds at higher densities, a situation that can be critical in real life and in which the pedestrians’ choices of motion are strongly constrained sterically. More precisely, we study the crowd’s response to its crossing by a pedestrian or a cylindrical obstacle of 74cm in diameter. In the latter case, for a moderately dense crowd, we observe displacements that quickly decay with the minimal distance to the obstacle, over a lengthscale of the order of the meter

Autonomous computational intelligence-based behaviour recognition in security and surveillance

This paper presents a novel approach to sensing both suspicious, and task-specific behaviours through the use of advanced computational intelligence techniques. Locating suspicious activity in surveillance camera networks is an intensive task due to the volume of information and large numbers of camera sources to monitor. This results in countless hours of video data being streamed to disk without being screened by a human operator. To address this need, there are emerging video analytics solutions that have introduced new metrics such as people counting and route monitoring, alongside more traditional alerts such as motion detection. There are however few solutions that are sufficiently robust to reduce the need for human operators in these environments, and new approaches are needed to address the uncertainty in identifying and classifying human behaviours, autonomously, from a video stream. In this work we present an approach to address the autonomous identification of human behaviours derived from human pose analysis. Behavioural recognition is a significant challenge due to the complex subtleties that often make up an action; the large overlap in cues results in high levels of classification uncertainty. False alarms are significant impairments to autonomous detection and alerting systems, and over reporting can lead to systems being muted, disabled, or decommissioned. We present results on a Computational-Intelligence based Behaviour Recognition (CIBR) that utilises artificial intelligence to learn, optimise, and classify human activity. We achieve this through extraction of skeleton recognition of human forms within an image. A type-2 Fuzzy logic classifier then converts the human skeletal forms into a set of base atomic poses (standing, walking, etc.), after which a Markov-chain model is used to order a pose sequence. Through this method we are able to identify, with good accuracy, several classes of human behaviour that correlate with known suspicious, or anomalous, behaviours

People detection and tracking using a network of low-cost depth cameras

Automaattinen ihmisten havainnointi on jo laajalti käytetty teknologia, jolla on sovelluksia esimerkiksi kaupan ja turvallisuuden aloilla. Tämän diplomityön tarkoituksena on suunnitella yleiskäyttöinen järjestelmä ihmisten havainnointiin sisätiloissa. Tässä työssä ensin esitetään kirjallisuudesta löytyvät ratkaisut ihmisten havainnointiin, seurantaan ja tunnistamiseen. Painopiste on syvyyskuvaa hyödyntävissä havaitsemismenetelmissä. Lisäksi esittellään kehitetty älykkäiden syvyyskameroiden verkko. Havainnointitarkkuutta kokeillaan neljällä kuvasarjalla, jotka sisältävät yli 20 000 syvyyskuvaa. Tulokset ovat lupaavia ja näyttävät, että yksinkertaiset ja laskennallisesti kevyet ratkaisut sopivat hyvin käytännön sovelluksiin.Automatic people detection is a widely adopted technology that has applications in retail stores, crowd management and surveillance. The goal of this work is to create a general purpose people detection framework. First, studies on people detection, tracking and re-identification are reviewed. The emphasis is on people detection from depth images. Furthermore, an approach based on a network of smart depth cameras is presented. The performance is evaluated with four image sequences, totalling over 20 000 depth images. Experimental results show that simple and lightweight algorithms are very useful in practical applications