The Multi-agent Simulation-based Framework for Optimization of Detectors Layout in Public Crowded Places

AbstractIn this work the framework for detectors layout optimization based on a multi-agent simulation is proposed. Its main intention is to provide a decision support team with a tool for automatic design of social threat detection systems for public crowded places. Containing a number of distributed detectors, this system performs detection and an identification of threat carriers. The generic model of detector used in the framework allows to consider detection of various types of threats, e.g. infections, explosives, drugs, radiation. The underlying agent-based models provide data on social mobility, which is used along with a probability based quality assessment model within the optimization process. The implemented multi-criteria optimization scheme is based on a genetic algorithm. For experimental study the framework has been applied in order to get the optimal detectors’ layout in Pulkovo airport

Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments

Analysis-by-synthesis: Pedestrian tracking with crowd simulation models in a multi-camera video network

For tracking systems consisting of multiple cameras with overlapping field-of-views, homography-based approaches are widely adopted to significantly reduce occlusions among pedestrians by sharing information among multiple views. However, in these approaches, the usage of information under real-world coordinates is only at a preliminary level. Therefore, in this paper, a multi-camera tracking system with integrated crowd simulation is proposed in order to explore the possibility to make homography information more helpful. Two crowd simulators with different simulation strategies are used to investigate the influence of the simulation strategy on the final tracking performance. The performance is evaluated by multiple object tracking precision and accuracy (MOTP and MOTA) metrics, for all the camera views and the results obtained under real-world coordinates. The experimental results demonstrate that crowd simulators boost the tracking performance significantly, especially for crowded scenes with higher density. In addition, a more realistic simulation strategy helps to further improve the overall tracking result

Detección de personas en presencia de grupos

La visión por computador es una rama de la informática, donde se trata de dotar a las máquinas de ese estimable sentido que es la vista. Esto unido a la inteligencia artificial, donde se quiere conseguir que un computador tenga consciencia, puede lograr que de manera automática y autónoma un ordenador pueda, por ejemplo, vigilar la seguridad de las personas. Por otro lado, la vídeo-vigilancia puede procesar diferentes tipos de entradas para generar alertas que un usuario pueda utilizar para evitar un insatisfactorio suceso. No hace falta decir que la vídeo-vigilancia no es nada sin la visión artificial, la cual es la encargada de hacer todo el procesamiento de las entradas que proporcionan las cámaras, y dar esas salidas que son las detecciones deseadas. Este proyecto consta de, por lo tanto, una unión de ambas. Debe de conseguir, utilizando diferentes herramientas, la transparente unificación de todos estos conceptos. Para ello, se quiere como objetivo final conseguir detectar personas de una manera eficaz cuando estas están en grupos de mayor o menor dimensión. Concretando, buscamos una mejora en la detección de personas en presencia de grupos. En este tipo de escenarios es aún más compleja la detección, dado que las personas pueden están altamente ocluidas unas con otras. Este proyecto lo podríamos catalogar como I+D+i. Dado que se investiga todo lo relacionado con la visión artificial y la vídeo-vigilancia. Se desarrolla un software a modo de prototipo que dispone de todas las funcionalidades deseadas. Y también se innova en la implementación propia de diferentes algoritmos. Por último, es importante recalcar que la principal vocación del presente trabajo es sentar las bases para el desarrollo posterior de diferentes aplicaciones relacionadas con la monitorización automática de escenas tanto desde el punto de vista de la implementación eficiente como desde el punto de vista del diseño de nuevos algoritmos de detección.Computer vision is a field in computer science, which tries to provide machines with that appreciable sense that is sight. This coupled with artificial intelligence, where you want a computer to have consciousness, can make automatically and autonomously that computers, for example, monitor people safety. Furthermore, video surveillance can process different types of inputs to generate alerts that a user can use to avoid an unsatisfactory event. Needless to say that video surveillance is nothing without the artificial vision, which is responsible for all of the processing of the inputs, which the cameras provide, and give those outputs, which are the desire detections. This project involves, therefore, a junction of the two issues. It must achieve, using different tools, the transparent unification of all these concepts. Therefore, our final aim is to detect people effectively in crowd scenes. Specifying, we search an improvement in the people detection in the presence of the groups. In this type of scenario is even more complex detection, since people can are highly occluded with other persons. This project could be included as a I+D+i type. Since everything related to computer vision and video surveillance is investigated. Specific software is developed as a prototype that has all the desired features. And also innovates in the actual implementation of different algorithms. Finally, make clear that what we want is to be helpful to different projects that linked to this one, can increase knowledge, effectiveness and efficiency of different investigations and future implementations. Finally, it is important to stress that the main vocation of the present work is to as much lay the foundations for the later development of different applications related to the automatic monitoring from scenes from the point of view of the efficient implementation as from the point of view of the design of new algorithms of detection

Motion prediction and interaction localisation of people in crowds

PhDThe ability to analyse and predict the movement of people in crowded scenarios can be of fundamental importance for tracking across multiple cameras and interaction localisation. In this thesis, we propose a person re-identification method that takes into account the spatial location of cameras using a plan of the locale and the potential paths people can follow in the unobserved areas. These potential paths are generated using two models. In the first, people’s trajectories are constrained to pass through a set of areas of interest (landmarks) in the site. In the second we integrate a goal-driven approach to the Social Force Model (SFM), initially introduced for crowd simulation. SFM models the desire of people to reach specific interest points (goals) in a site, such as exits, shops, seats and meeting points while avoiding walls and barriers. Trajectory propagation creates the possible re-identification candidates, on which association of people across cameras is performed using spatial location of the candidates and appearance features extracted around a person’s head. We validate the proposed method in a challenging scenario from London Gatwick airport and compare it to state-of-the-art person re-identification methods. Moreover, we perform detection and tracking of interacting people in a framework based on SFM that analyses people’s trajectories. The method embeds plausible human behaviours to predict interactions in a crowd by iteratively minimising the error between predictions and measurements. We model people approaching a group and restrict the group formation based on the relative velocity of candidate group members. The detected groups are then tracked by linking their centres of interaction over time using a buffered graph-based tracker. We show how the proposed framework outperforms existing group localisation techniques on three publicly available datasets