Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

Visual surveillance by dynamic visual attention method

This paper describes a method for visual surveillance based on biologically motivated dynamic visual attention in video image sequences. Our system is based on the extraction and integration of local (pixels and spots) as well as global (objects) features. Our approach defines a method for the generation of an active attention focus on a dynamic scene for surveillance purposes. The system segments in accordance with a set of predefined features, including gray level, motion and shape features, giving raise to two classes of objects: vehicle and pedestrian. The solution proposed to the selective visual attention problem consists of decomposing the input images of an indefinite sequence of images into its moving objects, defining which of these elements are of the user\\s interest at a given moment, and keeping attention on those elements through time. Features extraction and integration are solved by incorporating mechanisms of charge and discharge?based on the permanency effect?, as well as mechanisms of lateral interaction. All these mechanisms have proved to be good enough to segment the scene into moving objects and background

Incremental recognition of traffic situations from video image sequences

Our image evaluation system Xtrack tracks multiple-vehicle-configurations in image sequences. The resulting geometric state descriptions are associated with fuzzy attributes and relations and thereby form the basis for incremental characterization of traffic situations from the point of view of selected road users or observers. Knowledge representation and inference is performed by means of Fuzzy Metric Temporal Logic (FMTL) in order to provide an in-depth analyzable transition from raw video data to conceptual descriptions of traffic situations

Visión artificial aplicada a los sistemas de transporte inteligentes: aplicaciones prácticas

182 p.Esta tesis se focaliza en diferentes investigaciones y desarrollos llevados a cabo en el ámbito de los sistemas de transporte inteligente a diferentes niveles. Se han enfrentado problemas como la segmentación de vehículos, la detección y reconocimiento de elementos intrínsecamente relacionados con la infraestructura vial contemplando la posibilidad de extender esta detección a elementos ajenos a la infraestructura que pudieran generar situaciones de peligro si irrumpiesen de manera fortuita en las vías de transporte y se han realizado también, estudios teóricos sobre temas concretos que pueden actuar de guía para investigaciones realizadas en las líneas analizadas.En lo referente a los campos de aplicación, se proponen soluciones en diferentes áreas relacionadas con los sistemas de transporte inteligente. En concreto, soluciones para el peaje en sombra, donde se perseguían los objetivos de detección, clasificación y estimación de velocidad de los vehículos que transitaban una vía, soluciones para sistemas de asistencia avanzada a la conducción como el reconocimiento de señales de tráfico para ajustar la velocidad del vehículo e informar al conductor

Planung kooperativer Fahrmanöver für kognitive Automobile

Fahrerassistenzsysteme eröffnen die Möglichkeit für automatische Eingriffe in Gefahrensituationen und bieten dadurch ein Potenzial zur Unfallvermeidung und zur Minimierung der Unfallschwere im Straßenverkehr. Die Handlungen mehrerer kognitiver Fahrzeuge können über Funkkommunikation miteinander koordiniert werden. Diese Dissertation untersucht potenziell echtzeitfähige Bewegungsplanungsalgorithmen zur Planung von Fahrmanövern, die von mehreren Fahrzeugen kooperativ ausgeführt werden können

Visual recognition of multi-agent action

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1999.Includes bibliographical references (p. 167-184).Developing computer vision sensing systems that work robustly in everyday environments will require that the systems can recognize structured interaction between people and objects in the world. This document presents a new theory for the representation and recognition of coordinated multi-agent action from noisy perceptual data. The thesis of this work is as follows: highly structured, multi-agent action can be recognized from noisy perceptual data using visually grounded goal-based primitives and low-order temporal relationships that are integrated in a probabilistic framework. The theory is developed and evaluated by examining general characteristics of multi-agent action, analyzing tradeoffs involved when selecting a representation for multi-agent action recognition, and constructing a system to recognize multi-agent action for a real task from noisy data. The representation, which is motivated by work in model-based object recognition and probabilistic plan recognition, makes four principal assumptions: (1) the goals of individual agents are natural atomic representational units for specifying the temporal relationships between agents engaged in group activities, (2) a high-level description of temporal structure of the action using a small set of low-order temporal and logical constraints is adequate for representing the relationships between the agent goals for highly structured, multi-agent action recognition, (3) Bayesian networks provide a suitable mechanism for integrating multiple sources of uncertain visual perceptual feature evidence, and (4) an automatically generated Bayesian network can be used to combine uncertain temporal information and compute the likelihood that a set of object trajectory data is a particular multi-agent action. The recognition algorithm is tested using a database of American football play descriptions. A system is described that can recognize single-agent and multi-agent actions in this domain given noisy trajectories of object movements. The strengths and limitations of the recognition system are discussed and compared with other multi-agent recognition algorithms.by Stephen Sean Intille.Ph.D