Image Understanding for Prevention of Vandalism in Metro Stations

Chleq, Nicolas; Brémond, François; Thonnat, M.

doi:10.1007/978-1-4615-5085-3_10

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…The scene interpretation works in terms of high level behaviors and it is based on Bayesian networks. Another example applied to security in metro stations is presented by Chleq [2]. In this case, high level descriptions for different situations are handled using a scenario database.…”

mentioning

confidence: 99%

Image Analysis for Video Surveillance Based on Spatial Regularization of a Statistical Model-Based Change Detection

Ziliani

Cavallaro

2001

Real-Time Imaging

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confidence: 99%

Image Analysis for Video Surveillance Based on Spatial Regularization of a Statistical Model-Based Change Detection

Ziliani

Cavallaro

2001

Real-Time Imaging

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“…Most of the previous works have defined context in terms of the interaction of targets with static objects in the environment [15]. An elaborate and detailed discussion on the role of context in behavior analysis and video understanding can be found in [1,2]. A methodology for recognizing multi agents behavior in the game of American football was presented by Intille and Bobick in [6,7].…”

Section: Introductionmentioning

confidence: 99%

Behavior interpretation from traffic video streams

Kumar¹,

Ranganath²,

Sengupta³

2003

Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems

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This paper considers video surveillance applied to traffic video streams. We present a framework for analyzing and recognizing different traffic behaviors from image sequences acquired from a fixed camera. Two types of interactions have been mainly considered. In one there is interaction between two or more mobile objects in the Field of View (FOV) of the camera. The other is interaction between mobile objects and static objects in the environment. The framework is based on two types of a priori information: (1) the contextual information of the camera's FOV, in terms of the description of the different static objects of the scene and (2) sets of predefined behavior scenarios, which need to be analyzed in different contexts. At present the system is designed to recognize behavior from stored videos and retrieve the frames in which the specific behaviors took place. We demonstrate successful behavior recognition results for pedestrian-vehicle interaction and vehicle-checkpost interactions.

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“…A behavior is represented by a set of rules based with a stochastic contextfree grammar, which allows certain combinations of simple constant predicates. The general scheme of our approach is based on the use of predefined scenarios [6] and a priori contextual information [5]. In this paper we propose a method based on both n-ary tree to declare events [17] and temporal logics to declare application dependent scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…For each particular camera looking at a particular environment, a security operator must provide, in a configuration phase, pertinent information according to the formalism we propose. For more details on the role of context in video understanding see [5]. The context contains in addition to geometric information some semantic information.…”

Section: Contextual Informationmentioning

confidence: 99%