Intégration de la saillance visuelle dans la reconnaissance d’évènements rares

This paper presents a new method for the detection of rares events in video. It is based on the visual saliency and on the detection and local description of points of interest. The point-of-interest filtering is carried out using the saliency score, allowing only those with visual importance to be considered. A model of normal events is learned thanks to the probabilistic generative model "Latent Dirichlet Allocation" (LDA), known for its performance in textual data mining. The detection of an abnormal or rare event is carried out in a probabilistic way via the learned model. This paper proposes to combine a saliency based visual focalization and the use of automatic document classification technic in order to classify images from a video and to detect rare events

A very simple framework for 3D human poses estimation using a single 2D image: Comparison of geometric moments descriptors.

In this paper, we propose a framework in order to automatically extract the 3D pose of an individual from a single silhouette image obtained with a classical low-cost camera without any depth information. By pose, we mean the configuration of human bones in order to reconstruct a 3D skeleton representing the 3D posture of the detected human. Our approach combines prior learned correspondences between silhouettes and skeletons extracted from simulated 3D human models publicly available on the internet. The main advantages of such approach are that silhouettes can be very easily extracted from video, and 3D human models can be animated using motion capture data in order to quickly build any movement training data. In order to match detected silhouettes with simulated silhouettes, we compared geometrics invariants moments. According to our results, we show that the proposed method provides very promising results with a very low time processing

3D Human Poses Estimation from a Single 2D Silhouette

This work focuses on the problem of automatically extracting human 3D poses from a single 2D image. By pose we mean the configuration of human bones in order to reconstruct a 3D skeleton representing the 3D posture of the detected human. This problem is highly non-linear in nature and confounds standard regression techniques. Our approach combines prior learned correspondences between silhouettes and skeletons extracted from 3D human models. In order to match detected silhouettes with simulated silhouettes, we used Krawtchouk geometric moment as shape descriptor. We provide quantitative results for image retrieval across different action and subjects, captured from differing viewpoints. We show that our approach gives promising result for 3D pose extraction from a single silhouette

Bayesian Generative Model Based on Color Histogram of Oriented Phase and Histogram of Oriented Optical Flow for Rare Event Detection in Crowded Scenes

In this paper, we propose a new method for rare event detection in crowded scenes using a combination of Color Histogram of Oriented Phases (CHOP) and Histogram of Oriented Optical Flow (HOOF). We propose to detect and filter spatio-temporal interest points (STIP) based on the visual saliency information of the scene. Once salient STIPs are detected, the motion and appearance information of the surrounding scene are extracted. Finally, the extracted information from normal scenes are modelled by using a Bayesian generative model (Latent Dirichlet Allocation). The rare events are detected by processing the likelihood of the current scene in regard to the obtained model. The proposed method has been tested on the publicly available UMN dataset and compared with different state-of-the-art algorithms. We have shown that our method is very competitive and provides promising results

Intégration de la saillance visuelle dans la reconnaissance d’évènements rares

National audienceThis paper presents a new method for the detection of rares events in video. It is based on the visual saliency and on the detection and local description of points of interest. The point-of-interest filtering is carried out using the saliency score, allowing only those with visual importance to be considered. A model of normal events is learned thanks to the probabilistic generative model "Latent Dirichlet Allocation" (LDA), known for its performance in textual data mining. The detection of an abnormal or rare event is carried out in a probabilistic way via the learned model. This paper proposes to combine a saliency based visual focalization and the use of automatic document classification technic in order to classify images from a video and to detect rare events

Bayesian Generative Model Based on Color Histogram of Oriented Phase and Histogram of Oriented Optical Flow for Rare Event Detection in Crowded Scenes

International audienc

Intégration de la saillance visuelle dans la reconnaissance d’évènements rares

National audienceThis paper presents a new method for the detection of rares events in video. It is based on the visual saliency and on the detection and local description of points of interest. The point-of-interest filtering is carried out using the saliency score, allowing only those with visual importance to be considered. A model of normal events is learned thanks to the probabilistic generative model "Latent Dirichlet Allocation" (LDA), known for its performance in textual data mining. The detection of an abnormal or rare event is carried out in a probabilistic way via the learned model. This paper proposes to combine a saliency based visual focalization and the use of automatic document classification technic in order to classify images from a video and to detect rare events

Abnormal Events Analysis By The Latent Dirichlet Allocation (LDA)

International audienc

Abnormal Events Analysis By The Latent Dirichlet Allocation (LDA)

International audienc

Bayesian Generative Model Based on Color Histogram of Oriented Phase and Histogram of Oriented Optical Flow for Rare Event Detection in Crowded Scenes

International audienc