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This paper presents a new scenario recognition algorithm for Video Interpretation. We represent a scenario model by specifying the characters involved in the scenario, the sub-scenarios composing the scenario and the constraints combining the sub-scenarios. Various types of constraints can be used including spatio-temporal and logical constraints. In this(More)
In many surveillance systems there is a requirement to determine whether a given person of interest has already been observed over a network of cameras. This is the person re-identification problem. The human appearance obtained in one camera is usually different from the ones obtained in another camera. In order to re-identify people the human signature(More)
Human re-identification is defined as a requirement to determine whether a given individual has already appeared over a network of cameras. This problem is particularly hard by significant appearance changes across different camera views. In order to re-identify people a human signature should handle difference in illumination, pose and camera parameters.(More)
We propose an activity-monitoring framework based on a platform called VSIP, enabling behavior recognition in different environments. To allow end-users to actively participate in the development of a new application, VSIP separates algorithms from a priori knowledge. To describe how VSIP works, we present a full description of a system developed with this(More)
We present a new representation and recognition method for human activities. An activity is considered to be composed of action threads, each thread being executed by a single actor. A single-thread action is represented by a stochastic finite automaton of event states, which are recognized from the characteristics of the trajectory and shape of moving blob(More)
ETISEO [1] was a two year project (ended in December 2006) on performance evaluation for video surveillance systems. ETISEO aims at helping algorithm developers to identify algorithm weaknesses and to underline the dependencies between algorithms and their conditions of use. More precisely, ETISEO aims at evaluating video processing algorithms given a video(More)
This paper presents a real-time video understanding system which automatically recognises activities occurring in environments observed through video surveillance cameras. Our approach consists in three main stages: Scene Tracking, Coherence Maintenance, and Scene Understanding. The main challenges are to provide a robust tracking process to be able to(More)
This paper addresses the problem of appearance matching across disjoint camera views. Signicant appearance changes, caused by variations in view angle, illumination and object pose, make the problem challenging. We propose to formulate the appearance matching problem as the task of learning a model that selects the most descriptive features for a specic(More)
The goal of this paper is to describe and demonstrate the application of Bayesian networks in a generic automatic video surveillance system. Taking image features of tracked moving regions from an image sequence as input, mobile object properties are first computed and noise is suppressed by statistical methods. The probability that a scenario occurs is(More)
Crowd behavior recognition is becoming an important research topic in video surveillance for public places. In this paper, we first discuss the crowd feature selection and extraction and propose a multiple-frame feature point detection and tracking based on the KLT tracker. We state that behavior modelling of crowd is usually coarse compared to that for(More)