Virtu4D : A dynamic audio-video virtual representation for surveillance systems

Baklouti, Malek; Chamfrault, Marthe; Boufarguine, Mourad; Guitteny, Vincent

doi:10.1109/icscs.2009.5412287

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…The object projection method extracts video semantic objects from the original video through object detection. Specific implementation methods are divided into three types: (1) foreground and background independent projection [28,39]; (2) foreground projection [31,40]; and (3) foreground abstraction [41]. The foreground and background independent projections project the foreground moving object subgraphs to their corresponding spatial-temporal position in the virtual scene.…”

Section: Related Workmentioning

confidence: 99%

Integration of Multi-Camera Video Moving Objects and GIS

Xie

Wang

Liu

et al. 2019

IJGI

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This work discusses the integration of multi-camera video moving objects (MCVO) and GIS. This integration was motivated by the characteristics of multi-camera videos distributed in the urban environment, namely, large data volume, sparse distribution and complex spatial–temporal correlation of MCVO, thereby resulting in low efficiency of manual browsing and retrieval of videos. To address the aforementioned drawbacks, on the basis of multi-camera video moving object extraction, this paper first analyzed the characteristics of different video-GIS Information fusion methods and investigated the integrated data organization of MCVO by constructing a spatial–temporal pipeline among different cameras. Then, the conceptual integration model of MCVO and GIS was proposed on the basis of spatial mapping, and the GIS-MCVO prototype system was constructed in this study. Finally, this study analyzed the applications and potential benefits of the GIS-MCVO system, including a GIS-based user interface on video moving object expression in the virtual geographic scene, video compression storage, blind zone trajectory deduction, retrieval of MCVO, and video synopsis. Examples have shown that the integration of MCVO and GIS can improve the efficiency of expressing video information, achieve the compression of video data, rapidly assisting the user in browsing video objects from multiple cameras.

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Section: Related Workmentioning

confidence: 99%

Integration of Multi-Camera Video Moving Objects and GIS

Xie

Wang

Liu

et al. 2019

IJGI

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“…The reconstructed space aims at increasing scene understanding and people localization inside the scene. A similar approach was envisaged by [11] that added audio cues and time analysis for the events in a surveillance network, allowing a more precise localization of subjects within the video sensor network. Another approach employed in large-scale systems enforces situation awareness by linking cameras with the maps of the building.…”

Section: Introductionmentioning

confidence: 96%

Synthetic and privacy-preserving visualization of video sensor network outputs

Velardo

Araimo

Dugelay

2011

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras

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We propose a complete framework for people tracking in video surveillance networks that preserves privacy by mapping people recorded by a video sensor network in the map of the corresponding surveilled areas. Thanks to this approach, it is possible (1) to synthesize multiple video outputs into a unique picture, and (2) to control privacy by offering the possibility to filter information to viewers. A set of physical attributes, namely soft biometric characteristics, is used to provide the system with tracking capabilities. We demonstrate the feasibility of our approach by showing a real case application scenario.

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“…GIS-video object fusion combines video background and video moving objects in a geographic space [23]. Based on the differences of video moving objects formation in a virtual scene, GIS-video object fusion can be divided into different subclasses, such as foreground and background scene independent projection [14,24], foreground projection [15], and foreground abstract projection [25].…”

Section: Introductionmentioning

confidence: 99%

Surveillance Video Synopsis in GIS

Xie

Wang

Liu

et al. 2017

IJGI

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Surveillance videos contain a considerable amount of data, wherein interesting information to the user is sparsely distributed. Researchers construct video synopsis that contain key information extracted from a surveillance video for efficient browsing and analysis. Geospatial-temporal information of a surveillance video plays an important role in the efficient description of video content. Meanwhile, current approaches of video synopsis lack the introduction and analysis of geospatial-temporal information. Owing to the preceding problems mentioned, this paper proposes an approach called "surveillance video synopsis in GIS". Based on an integration model of video moving objects and GIS, the virtual visual field and the expression model of the moving object are constructed by spatially locating and clustering the trajectory of the moving object. The subgraphs of the moving object are reconstructed frame by frame in a virtual scene. Results show that the approach described in this paper comprehensively analyzed and created fusion expression patterns between video dynamic information and geospatial-temporal information in GIS and reduced the playback time of video content.

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Virtu4D : A dynamic audio-video virtual representation for surveillance systems

Cited by 6 publications

References 15 publications

Integration of Multi-Camera Video Moving Objects and GIS

Integration of Multi-Camera Video Moving Objects and GIS

Synthetic and privacy-preserving visualization of video sensor network outputs

Surveillance Video Synopsis in GIS

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