Ahmed AZOUGH, auteur/autrice sur Pôle Léonard de Vinci

Mohamed Zaifri; Hamza Khalloufi; Fatima Zahra Kaghat; Abdessamad Benlahbib; Ahmed Azough; Khalid Alaoui Zidani

Enhancing Tourist Experiences in Crowded Destinations through Mobile Augmented Reality: A Comparative Field Study Article de journal

Dans: International Journal of Interactive Mobile Technologies, vol. 17, no. Vol. 17 No, p. 92-113, 2023.

Résumé | Liens | BibTeX

@article{zaifri_2475,

title = {Enhancing Tourist Experiences in Crowded Destinations through Mobile Augmented Reality: A Comparative Field Study},

author = {Mohamed Zaifri and Hamza Khalloufi and Fatima Zahra Kaghat and Abdessamad Benlahbib and Ahmed Azough and Khalid Alaoui Zidani},

url = {https://online-journals.org/index.php/i-jim/article/view/42273},

year  = {2023},

date = {2023-11-01},

journal = {International Journal of Interactive Mobile Technologies},

volume = {17},

number = {Vol. 17 No},

pages = {92-113},

abstract = {Mobile augmented reality (MAR) has gained significant attention in the tourism sector as a way to enhance the visitor experience. The rapid advancements in mobile computing and sensor technologies have facilitated the widespread use of geospatial augmented reality (AR) applications by tourists when exploring popular destinations. To analyze the impact of AR technology on the tourism experience, we developed the FEZAR mobile application. This application serves as the focal point of our study, allowing us to evaluate user performance using a comparative experimental approach. To ensure the usability of the FEZAR application, professionals with expertise in mobile technologies, including AR, performed rigorous testing and evaluation of the application. Through their evaluations, significant usability issues were identified and resolved, resulting in the application being well-received by the experts. Subsequently, a comparative field study was conducted in Fez's old medina, a crowded UNESCO heritage site, involving users (N = 40) who were randomly assigned to experimental and control groups in equal distribution. The results of the study revealed that the proposed AR model had a significant positive impact on user visits. Compared to other forms of media, AR offers more informative and enjoyable experiences. Additionally, it effectively helps locate monuments in crowded tourism settings. The findings of this research make a valuable contribution to the ongoing discussion regarding the impact of evaluating MAR user interfaces on increasing visitor engagement with tourist destinations.},

keywords = {},

pubstate = {online},

tppubtype = {article}

}

Fermer

Mohamed Zaifri; Hamza Khalloufi; Fatima Zahra Kaghat; Ahmed Azough; Khalid Alaoui Zidani

From Earlier Exploration to Advanced Applications: Bibliometric and Systematic Review of Augmented Reality in the Tourism Industry (2002-2022) Article de journal

Dans: Multimodal Technologies and Interaction, vol. 7, no. 7, p. 64, 2023.

Résumé | Liens | BibTeX

@article{zaifri_2354,

title = {From Earlier Exploration to Advanced Applications: Bibliometric and Systematic Review of Augmented Reality in the Tourism Industry (2002-2022)},

author = {Mohamed Zaifri and Hamza Khalloufi and Fatima Zahra Kaghat and Ahmed Azough and Khalid Alaoui Zidani},

url = {https://www.mdpi.com/2414-4088/7/7/64},

year  = {2023},

date = {2023-06-01},

journal = {Multimodal Technologies and Interaction},

volume = {7},

number = {7},

pages = {64},

abstract = {Augmented reality has emerged as a transformative technology, with the potential to revolutionize the tourism industry. Nonetheless, there is a scarcity of studies tracing the progression of AR and its application in tourism, from early exploration to recent advancements. This study aims to provide a comprehensive overview of the evolution, contexts, and design elements of AR in tourism over the period (2002-2022), offering insights for further progress in this domain. Employing a dual-method approach, a bibliometric analysis was conducted on 861 articles collected from the Scopus and Web of Science databases, to investigate the evolution of AR research over time and across countries, and to identify the main contexts of the utilization of AR in tourism. In the second part of our study, a systematic content analysis was conducted, focusing on a subset of 57 selected studies that specifically employed AR systems in various tourism situations. Through this analysis, the most commonly utilized AR design components, such as tracking systems, AR devices, tourism settings, and virtual content were summarized. Furthermore, we explored how these components were integrated to enhance the overall tourism experience. The findings reveal a growing trend in research production, led by Europe and Asia. Key contexts of AR applications in tourism encompass cultural heritage, mobile AR, and smart tourism, with emerging topics such as artificial intelligence (AI), big data, and COVID-19. Frequently used AR design components comprise mobile devices, marker-less tracking systems, outdoor environments, and visual overlays. Future research could involve optimizing AR experiences for users with disabilities, supporting multicultural experiences, integrating AI with big data, fostering sustainability, and remote virtual tourism. This study contributes to the ongoing discourse on the role of AR in shaping the future of tourism in the post COVID-19 era, by providing valuable insights for researchers, practitioners, and policymakers in the tourism industry.},

note = {- CiteScore 2022 : 4.3,

- SJR 2022 : 0.504,

- SNIP 2022 : 1.163

- Q2 (computer science applications)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Fermer

Augmented reality has emerged as a transformative technology, with the potential to revolutionize the tourism industry. Nonetheless, there is a scarcity of studies tracing the progression of AR and its application in tourism, from early exploration to recent advancements. This study aims to provide a comprehensive overview of the evolution, contexts, and design elements of AR in tourism over the period (2002-2022), offering insights for further progress in this domain. Employing a dual-method approach, a bibliometric analysis was conducted on 861 articles collected from the Scopus and Web of Science databases, to investigate the evolution of AR research over time and across countries, and to identify the main contexts of the utilization of AR in tourism. In the second part of our study, a systematic content analysis was conducted, focusing on a subset of 57 selected studies that specifically employed AR systems in various tourism situations. Through this analysis, the most commonly utilized AR design components, such as tracking systems, AR devices, tourism settings, and virtual content were summarized. Furthermore, we explored how these components were integrated to enhance the overall tourism experience. The findings reveal a growing trend in research production, led by Europe and Asia. Key contexts of AR applications in tourism encompass cultural heritage, mobile AR, and smart tourism, with emerging topics such as artificial intelligence (AI), big data, and COVID-19. Frequently used AR design components comprise mobile devices, marker-less tracking systems, outdoor environments, and visual overlays. Future research could involve optimizing AR experiences for users with disabilities, supporting multicultural experiences, integrating AI with big data, fostering sustainability, and remote virtual tourism. This study contributes to the ongoing discourse on the role of AR in shaping the future of tourism in the post COVID-19 era, by providing valuable insights for researchers, practitioners, and policymakers in the tourism industry.

Fermer

Pierre Lefebvre; Ahmed Azough; Nicolas Travers; Driss Yakoubi

Vid2Graph : un framework pour l'extraction de connaissances et l'analyse sémantique des flux de vidéosurveillance de systèmes distribués Conférence

GdR Madics, Troyes, 2023.

Résumé | Liens | BibTeX

@conference{lefebvre_2306,

title = {Vid2Graph : un framework pour l'extraction de connaissances et l'analyse sémantique des flux de vidéosurveillance de systèmes distribués},

author = {Pierre Lefebvre and Ahmed Azough and Nicolas Travers and Driss Yakoubi},

url = {https://www.dataia.eu/evenements/5eme-symposium-gdr-madics},

year  = {2023},

date = {2023-05-01},

booktitle = {GdR Madics},

address = {Troyes},

abstract = {La vidéosurveillance s'est considérablement développée ces dernières années. Les sources sont de plus en plus nombreuses, en mouvement ou avec des qualités variables : on parle de système distribué. L'analyse des données produites par de tels systèmes est devenue un enjeu majeur. En effet, si la détection des objets et des actions capturés par une caméra individuelle est aujourd'hui accessible à travers les modèles d'apprentissage automatique ou de reconnaissance de forme, la modélisation et la détection automatique d'évènements longue durée et faisant intervenir un réseau de caméras de surveillance restent un défi. 

Ainsi, comment permettre la détection d'évènements complexes dans un réseau de caméras de surveillance hétérogènes et distribuées ?

Afin de répondre à cette problématique, nous proposons un framework pour l'extraction et l'enrichissement de caractéristiques à partir de caméras de vidéosurveillance. Il repose sur 1) un pipeline de modèles de Deep Learning pour l'extraction de caractéristiques de vidéos (extraction d'images-clés, détections d'objets / segmentation d'instances, extraction d'attributs, détection de relations spatiales, réidentification), 2) un module de génération d'un graphe de connaissances, 3) un module d'enrichissement du graphe pour améliorer la qualité des détections, et 4) un module d'analyse pour la détection d'événements complexes sur le graph. Son architecture modulaire permet d'interchanger les étapes d'extraction de caractéristiques provenant des vidéos. Le poster détaillera le framework proposé et illustrera le processus de création du graphe à partir de vidéos provenant du benchmark  Smart-City CCTV Violence Detection Dataset (SCVD).

L'intérêt de l'approche est de pouvoir, à terme, se focaliser sur la sémantique des vidéos comme l'isolation de segments vidéo ou d'actions (filtres/projections sur le graph), la détection d'événements ou activités au moyen d'algorithmes de Graph Mining / GNN.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Fermer

La vidéosurveillance s'est considérablement développée ces dernières années. Les sources sont de plus en plus nombreuses, en mouvement ou avec des qualités variables : on parle de système distribué. L'analyse des données produites par de tels systèmes est devenue un enjeu majeur. En effet, si la détection des objets et des actions capturés par une caméra individuelle est aujourd'hui accessible à travers les modèles d'apprentissage automatique ou de reconnaissance de forme, la modélisation et la détection automatique d'évènements longue durée et faisant intervenir un réseau de caméras de surveillance restent un défi.
Ainsi, comment permettre la détection d'évènements complexes dans un réseau de caméras de surveillance hétérogènes et distribuées ?
Afin de répondre à cette problématique, nous proposons un framework pour l'extraction et l'enrichissement de caractéristiques à partir de caméras de vidéosurveillance. Il repose sur 1) un pipeline de modèles de Deep Learning pour l'extraction de caractéristiques de vidéos (extraction d'images-clés, détections d'objets / segmentation d'instances, extraction d'attributs, détection de relations spatiales, réidentification), 2) un module de génération d'un graphe de connaissances, 3) un module d'enrichissement du graphe pour améliorer la qualité des détections, et 4) un module d'analyse pour la détection d'événements complexes sur le graph. Son architecture modulaire permet d'interchanger les étapes d'extraction de caractéristiques provenant des vidéos. Le poster détaillera le framework proposé et illustrera le processus de création du graphe à partir de vidéos provenant du benchmark Smart-City CCTV Violence Detection Dataset (SCVD).
L'intérêt de l'approche est de pouvoir, à terme, se focaliser sur la sémantique des vidéos comme l'isolation de segments vidéo ou d'actions (filtres/projections sur le graph), la détection d'événements ou activités au moyen d'algorithmes de Graph Mining / GNN.

Fermer

Jihane Mali; Ahvar Shohreh; Faten Atigui; Ahmed Azough; Nicolas Travers

A Global Model-Driven Denormalization Approach for Schema Migration Proceedings Article

Dans: International Conference on Research Challenges in Information Science, p. 529-545, Springer, Barcelona, Spain, 2022.

Résumé | Liens | BibTeX