Nicolas Saunier

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Intelligent Transportation Systems need methods to automatically monitor the road traffic, and especially track vehicles. Most research has concentrated on highways. Traffic in intersections is more variable, with multiple entrance and exit regions. This paper describes an extension to intersections of the feature-tracking algorithm described in [1].(More)
In general, the problem of change detection is studied in color space. Most proposed methods aim at dynamically finding the best color thresholds to detect moving objects against a background model. Background models are often complex to handle noise affecting pixels. Because the pixels are considered individually, some changes cannot be detected because it(More)
This work aims at addressing the many problems that have hindered the development of vision-based systems for automated road safety analysis. The approach relies on traffic conflicts used as surrogates for collision data. Traffic conflicts are identified by computing the collision probability for any two road users in an interaction. A complete system is(More)
Road collisions represent deplorable human and financial costs to society. Although some progress has been made, a renewed effort is necessary to tackle this growing worldwide issue. This paper advocates the development of proactive methods for road safety analysis that do not depend on the occurrence of collisions. In particular, collecting and analyzing(More)
The importance of reducing the social and economic costs associated with traffic collisions can not be over-stated. The first goal of this research is to develop a method for automated road safety analysis using video sensors in order to address the problem of a dependency on the deteriorating collision data. The method will automate the extraction of(More)
We present a novel approach to background subtraction that is based on the local shape of small image regions. In our approach, an image region centered on a pixel is modeled using the local self-similarity descriptor. We aim at obtaining a reliable change detection based on local shape change in an image when foreground objects are moving. The method first(More)
In this paper, we derive incremental learning algorithms from a difficult real world task. Our application learns how to evaluate the risk of collision for road users at intersections, based on occupation measurements supplied by video sensors. The data are noisy and complex, and only a human expert can evaluate the risk on video recordings. In order to(More)
Urban areas in North American cities with positive trends in bicycle usage also witness a high number of cyclist injuries every year. Previous cyclist safety studies based on the traditional approach, which relies on historical crash data, are known to have some limitations such as the fact that crashes need to happen (a reactive approach). This paper(More)
In this paper, we study the problem of detecting and tracking multiple objects of various types in outdoor urban traffic scenes. This problem is especially challenging due to the large variation of road user appearances. To handle that variation, our system uses background subtraction to detect moving objects. In order to build the object tracks, an object(More)