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Détection et analyse des évènements rares par vision, dans un contexte urbain ou péri-urbain

Abstract : The main objective of this thesis is the development of complete methods for rare events detection. The works can be summarized in two parts. The first part is devoted to the study of shapes descriptors of the state of the art. On the one hand, the robustness of some descriptors to varying light conditions was studied.On the other hand, the ability of geometric moments to describe the human shape was also studied through a3D human pose estimation application based on 2D images. From this study, we have shown that through a shape retrieval application, geometric moments can be used to estimate a human pose through an exhaustive search in a pose database. This kind of application can be used in human actions recognition system which may be a final step of an event analysis system. In the second part of this report, three main contributions to rare event detection are presented. The first contribution concerns the development of a global scene analysis method for crowd event detection. In this method, global scene modeling is done based on spatiotemporal interest points filtered from the saliency map of the scene. The characteristics used are the histogram of the optical flow orientations and a set of shapes descriptors studied in the first part. The Latent Dirichlet Allocation algorithm is used to create event models by using a visual document representation of image sequences(video clip). The second contribution is the development of a method for salient motions detection in video.This method is totally unsupervised and relies on the properties of the discrete cosine transform to explore the optical flow information of the scene. Local modeling for events detection and localization is at the core of the latest contribution of this thesis. The method is based on the saliency score of movements and one class SVM algorithm to create the events model. The methods have been tested on different public database and the results obtained are promising.
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Submitted on : Monday, November 2, 2020 - 3:46:10 PM
Last modification on : Tuesday, November 3, 2020 - 3:21:12 AM


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  • HAL Id : tel-02985957, version 1


Dieudonne Fabrice Atrevi. Détection et analyse des évènements rares par vision, dans un contexte urbain ou péri-urbain. Autre. Université d'Orléans, 2019. Français. ⟨NNT : 2019ORLE2008⟩. ⟨tel-02985957⟩



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