Modeling metadata of CCTV systems and Indoor Location Sensors for automatic filtering of relevant video content

Panta, Franck Jeveme; Roman-Jimenez, Geoffrey; Sèdes, Florence

doi:10.1109/rcis.2018.8406677

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…This idea is supported by approaches found in the patent (U.S. Patent No. 15/486,109, 2018) and research papers (Panta, Roman-Jimenez & Sedes, 2018). In this sense, a new technological opportunity arises -it is possible to store semantic information rather than raw data.…”

Section: Previous Researchmentioning

confidence: 99%

Method for Real Time Face Recognition Application in Unmanned Aerial Vehicles

et al. 2019

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Newly evolving threats to public safety and security, related to attacks in public spaces, are catching the attention of both law enforcement and the general public. Such threats range from the emotional misbehaviour of sports fans in sports venues to well-planned terrorist attacks. Moreover, tools are needed to assist in the search for wanted persons. Static solutions, such as closed circuit television (CCTV), exist, but there is a need for a highly-portable, on-demand solution. Unmanned aerial vehicles (UAVs) have evolved drastically over the past decade. Developments are observed not only with regards to flight mechanisms and extended flight times but also in the imaging and image stabilization capabilities. Although different methods for facial recognition have existed for some time, dealing with imaging from a moving source to detect the faces in the crowd and compare them to an existing face database is a scientific problem that requires a complex solution. This paper deals with real-time face recognition in the crowd using unmanned aerial vehicles. Face recognition was performed using OpenCV and Dlib libraries.

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Section: Previous Researchmentioning

confidence: 99%

Method for Real Time Face Recognition Application in Unmanned Aerial Vehicles

et al. 2019

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“…Further, frequent object location changes and constant pedestrian movements also make indoor environments challenging. A wide range of technology can and has been used to support indoor positioning, including WLAN / WiFi, Bluetooth, RFID tags, and other sensors, particularly cameras and CCTV (see [2,4,20]). Chen and Chen [4] focus on smartphone technologies given the range of sensors they contain.…”

Section: Introductionmentioning

confidence: 99%

Assessing the influence of indoor mapping sources for indoor spatial analysis of physical distancing

Murray,

Baik,

Malak

2023

JOSIS

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GIScience and spatial information contributions to indoor mapping and navigation are many, but there remain significant challenges. Indoor environments are where people spend most of their time, socializing, working, learning, exercising, etc. During times of emergencies, disease outbreaks, and crises, indoor management and planning must be prepared to handle such events, yet doing so is often hindered by a lack of supporting spatial information and appropriate analytics. This paper focuses on COVID-19 mitigation measures to reduce disease transmission through physical distancing in indoor spaces, such as classrooms, offices, dining commons, restaurants, and entertainment venues. Geographical data to support indoor environments is discussed, particularly issues of acquisition, spatial data uncertainty, and implications for spatial analytics. Planning for classroom physical distancing on a university campus highlights capabilities, issues, and challenges, with a comparison made between previous studies relying on architectural data and more precise information obtained using LiDAR.

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Management of Mobile Objects Location for Video Content Filtering

Panta

Qodseya

Péninou

et al. 2018

Proceedings of the 16th International Conference on Advances in Mobile Computing and Multimedia

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The use of mobile devices and the development of geo-positioning technologies make applications that use location-based services very attractive and useful. These applications are composed of sensors that generate various and heterogeneous spatio-temporal data. Exploiting this spatio-temporal data to support video surveillance systems remains a relevant purpose for video content ltering. Since the data processed in such a context are heterogeneous (indoor and outdoor environment, various position types and reference systems, various data format), interoperability and management of these data remains a problem to be solved.In this paper, we de ne an approach that integrates camera location and eld of view metadata, mobile objects trajectories, and metadata from video content analysis algorithms (e.g. detection and movement of mobile objects) to address problems related to processing of huge amount of data (big data) generated by CCTV systems. We propose a new generic trajectory based query in order to handle trajectory segment's heterogeneity for both environments (indoor and outdoor). The proposed data model integrate multi-source metadata and enable to handle interoperability issue of data. Our querying mechanism enable to automatically retrieve video segments that could contain relevant information for the CCTV operator (suspects, trajectories, etc.).We provide an experimental evaluation demonstrating the utility of our approach in a real-world case. Results show that the proposed approach enhances the e ciency of investigators by reducing the search space, as the operator will analyze only the relevant data, therefore he needs less time for video processing (video reviewing).

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Modeling metadata of CCTV systems and Indoor Location Sensors for automatic filtering of relevant video content

Cited by 4 publications

References 14 publications

Method for Real Time Face Recognition Application in Unmanned Aerial Vehicles

Method for Real Time Face Recognition Application in Unmanned Aerial Vehicles

Assessing the influence of indoor mapping sources for indoor spatial analysis of physical distancing

Management of Mobile Objects Location for Video Content Filtering

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