Nicolas Saunier

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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 mod-eled 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(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(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)
— The importance of reducing the social and economic costs associated with traffic collisions can not be overstated. 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 introduce MMTrack (max-margin tracker), a single-target tracker that linearly combines constant and adaptive appearance features. We frame offline single-camera tracking as a structured output prediction task where the goal is to find a sequence of locations of the target given a video. Following recent advances in machine learning, we discriminatively(More)
In this paper, we introduce MMTrack, a hybrid single pedestrian tracking algorithm that puts together the advantages of descriptive and discriminative approaches for tracking. Specifically, we combine the idea of cluster-based appearance modeling and online tracking and employ a max-margin criterion for jointly learning the relative importance of different(More)
This paper proposes an original definition of the exposure to lateral collision in signalized intersections and discusses the results of a real world experiment. This exposure is defined as the duration of situations where the stream that is given the right-of-way goes through the conflict zone while road users are waiting in the cross-traffic approach.(More)
Intersections constitute one of the most dangerous elements in road systems. Traffic signals remain the most common way to control traffic at high-volume intersections and offer many opportunities to apply intelligent transportation systems to make traffic more efficient and safe. This paper describes an automated method to estimate the temporal exposure of(More)
This paper presents an original time-sensitive traffic management application for road safety diagnosis in signalized intersections. Such applications require to deal with data streams that may be subject to concept drift over various time scales. The method for road safety analysis relies on the estimation of severity indicators for vehicle interactions(More)
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