Counting and tracking people in a cameras’ network for behavioral analysis

L’étude et la compréhension du comportement humain est devenue l’une des problématiques majeures dans différents secteurs d’activités. Ce besoin de comprendre les habitudes des individus a conduit plusieurs entreprises vers l’utilisation de vidéos pour l’analyse et l’interprétation des comportements. Ces raisons ont conduit à l’émergence de travaux de recherches qui ont pour objectif l’automatisation de ces procédures. De ce fait, l’étude du comportement humain est devenue l’un des principaux sujets de recherche dans le domaine de la vision par ordinateur, et de nombreuses solutions d’analyse du comportements basées sur l’utilisation de l’intelligence artificielle ont émergé.Dans ce travail, notre objectif est le développement d’un système qui va permettre de suivre simultanément plusieurs individus dans un réseau multi-caméras dans le contexte de l’analyse comportementale. Pour cela, nous avons proposé un système de suivi qui est composé de trois modules principaux et d’un module de gestion. Le premier est un module de comptage pour mesurer les entrées. Le deuxième module, basé sur l’utilisation de filtres à particules, est un système de suivi mono-caméra destiné à suivre les individus indépendamment dans chacune des caméras. Le troisième module, basé sur la sélection des régions saillantes de chaque individu, sert à la ré-identification et permet d’associer les individus détectés dans les différentes caméras. Enfin, le module de gestion est conçu pour créer des trajectoires sémantiques à partir des trajectoires brutes obtenues précédemment.The study and the understanding of human behavior has become one of the major concerns for various reasons in different sectors of activity. This need to understand the habits of people led several big firms towards the use of videos surveillance for analyzing and interpreting behaviors. These reasons led to the emergence of research aimed at automating these procedures. As a result, the study of human behavior has become the main subject of several researches in the field of computer vision. Thus, a variety of behavior analysis solutions based on artificial intelligence emerged.In this work, our objective is the proposal of a solution that enable the simultaneous track of several individuals in a multi-camera network in order to reconstruct their trajectories in the context of behavioral analysis. For this, we have proposed a system that is made of three main modules and a management module. The first module is a counting module to measure entries. The second module is a mono-camera tracking system that is based on the use of particle filtering to track individuals independently in each camera. The third module is a re-identification module which is based on the selection of salient regions for each individual. It enables the association of the individuals that are detected in the different cameras. The last module which is the management module is based on the use of ontologies for interpreting trajectories. This module is designed to create semantic trajectories from raw trajectories obtained previously

People Counting based on Kinect Depth Data

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People’s Re-identification Across Multiple Non-overlapping Cameras by Local Discriminative Patch Matching

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An Improved Vision-Based Indoor Positioning Method

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Towards Designing a Li-Fi-Based Indoor Positioning and Navigation System in an IoT Context

International audiencePeople generally find themselves lost while visiting a new indoor location because they are unaware of the building's architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented

Enhancing Anomaly Detection in Melanoma Diagnosis Through Self-Supervised Training and Lesion Comparison

International audienceMelanoma, a highly aggressive form of skin cancer notorious for its rapid metastasis, necessitates early detection to mitigate complex treatment requirements. While considerable research has addressed melanoma diagnosis using convolutional neural networks (CNNs) on individual dermatological images, a deeper exploration of lesion comparison within a patient is warranted for enhanced anomaly detection, which often signifies malignancy. In this study, we present a novel approach founded on an automated, self-supervised framework for comparing skin lesions, working entirely without access to ground truth labels. Our methodology involves encoding lesion images into feature vectors using a state-of-the-art representation learner, and subsequently leveraging an anomaly detection algorithm to identify atypical lesions. Remarkably, our model achieves robust anomaly detection performance on ISIC 2020 without needing annotations, highlighting the efficacy of the representation learner in discerning salient image features. These findings pave the way for future research endeavors aimed at developing better predictive models as well as interpretable tools that enhance dermatologists' efficacy in scrutinizing skin lesions

People tracking in multi-camera systems: a review

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Post-processing of light sheet fluorescence microscope images using auto-encoders and Richardson-Lucy deconvolution

International audienceLight Sheet Fluorescence Microscopy (LSFM) is a powerful tool for neurobiologists, allowing high-quality fast volumetric imaging. However, the used imaging technique induces some artifacts and distortions on the reconstructed 3D volumes. Thus enhancing LSFM images before 3D reconstructions is a crucial step for high-quality reconstructions. In this study, we proposed a pipeline for enhancing the image quality slice by slice. To achieve this, we proposed a novel approach using a pipeline of three steps. We started by implementing a deblurring algorithm based on the work of [1] [2], followed by an automatic contrast enhancement. Then, in order to remove the noise accentuated by this contrast enhancement, we developed a convolutional denoising auto-encoder using skip-connections, providing outstanding results on mixed Poisson-Gaussian noise. Finaly, we addressed the issue of axial distortion occuring on LSFM. We proposed a novel approach based on an auto-encoder trained on bead calibration images. Our proposed pipeline provides a comprehensive solution with promising results surpassing existing methods for improving the quality of LSFM images, which can boost the interpretation of biological data

GAF-Net: Video-Based Person Re-Identification via Appearance and Gait Recognitions

International audienceVideo-based person re-identification (Re-ID) is a challenging task aiming to match individuals across various cameras based on video sequences. While most existing Re-ID techniques focus solely on appearance information, including gait information, could potentially improve person Re-ID systems. In this study, we propose, GAF-Net, a novel approach that integrates appearance with gait features for re-identifying individuals; the appearance features are extracted from RGB tracklets while the gait features are extracted from skeletal pose estimation. These features are then combined into a single feature allowing the re-identification of individuals. Our numerical experiments on the iLIDS-Vid dataset demonstrate the efficacy of skeletal gait features in enhancing the performance of person Re-ID systems. Moreover, by incorporating the state-of-the-art PiT network within the GAF-Net framework, we improve both rank-1 and rank-5 accuracy by 1 percentage point

In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification

International audienceThis study provides an in-depth analysis of GAF-Net, a novel model for video-based person re-identification (Re-ID) that matches individuals across different video sequences. GAF-Net combines appearance-based features with gait-based features derived from skeletal data, offering a new approach that diverges from traditional silhouette-based methods. We thoroughly examine each module of GAF-Net and explore various fusion methods at the both score and feature levels, extending beyond initial simple concatenation. Comprehensive evaluations on the iLIDS-VID and MARS datasets demonstrate GAF-Net's effectiveness across scenarios. GAF-Net achieves state-ofthe-art 93.2% rank-1 accuracy on iLIDS-VID's long sequences, while MARS results (86.09% mAP, 89.78% rank-1) reveal challenges with shorter, variable sequences in complex real-world settings. We demonstrate that integrating skeleton-based gait features consistently improves Re-ID performance, particularly with long, more informative sequences. This research provides crucial insights into multimodal feature integration in Re-ID tasks, laying a foundation for the advancement of multi-modal biometric systems for diverse computer vision applications