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

Progrès recent en analyse de sequences d'images

Cette publication a pour but de montrer que des methodes de minimisation présentées récemment sont un outil mathematique pour la recherche systematique de solutions aux problèmes poses par l'estimation de champs de vecteurs de déplacement. Nous présentons comme perspective d'avenir l'étude de problèmes tels que le choix d'une échelle appropriée pour la description de structures de niveaux gris et un meilleur traitement des changements-dans le temps. Nous soulignons les possibilites de faire une symbiose entre l'approche basée sur l'extraction de caracteristiques et celle basée sur l'utilisation d'une analyse des gradients de niveau gris en vue de l'estimation de vecteurs de deplacement

Integrating Behavior-based Prediciton for Tracking Vehicles in Traffic Videos

Road vehicles usually remain within marked lanes. Such an hypothesis reflects a longer temporal perspective than the frequently used assumption that a vehicle continues with the currently estimated speed and direction. We study the first, more general, hypothesis in particular to track road vehicles through extended periods of occlusion "without", however, relying on 3D-models of occluding foreground bodies. A potential onset of occlusion is detected by a fuzzy conjunction of large, "facet-specific" color changes and a low ratio of the number of pixels with a prediction-compatible Optical-Flow (OF) vector relative to the total number of pixels within a facet of the 3D-polyhedral vehicle model. Experimental results for the entire approach are presented

Integrating Behavior-based Prediciton for Tracking Vehicles in Traffic Videos

Road vehicles usually remain within marked lanes. Such an hypothesis reflects a longer temporal perspective than the frequently used assumption that a vehicle continues with the currently estimated speed and direction. We study the first, more general, hypothesis in particular to track road vehicles through extended periods of occlusion "without", however, relying on 3D-models of occluding foreground bodies. A potential onset of occlusion is detected by a fuzzy conjunction of large, "facet-specific" color changes and a low ratio of the number of pixels with a prediction-compatible Optical-Flow (OF) vector relative to the total number of pixels within a facet of the 3D-polyhedral vehicle model. Experimental results for the entire approach are presented

Integrating Behavior-based Prediciton for Tracking Vehicles in Traffic Videos

Road vehicles usually remain within marked lanes. Such an hypothesis reflects a longer temporal perspective than the frequently used assumption that a vehicle continues with the currently estimated speed and direction. We study the first, more general, hypothesis in particular to track road vehicles through extended periods of occlusion "without", however, relying on 3D-models of occluding foreground bodies. A potential onset of occlusion is detected by a fuzzy conjunction of large, "facet-specific" color changes and a low ratio of the number of pixels with a prediction-compatible Optical-Flow (OF) vector relative to the total number of pixels within a facet of the 3D-polyhedral vehicle model. Experimental results for the entire approach are presented