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Girgensohn, Andreas; Kimber, Don; Vaughan, Jim; Yang, Tao; Shipman, Frank M.; Turner, Thea; Rieffel, Eleanor; Wilcox, Lynn; Chen, Francine; Dunnigan, Tony

doi:10.1145/1291233.1291332

Cited by 51 publications

(2 citation statements)

References 22 publications

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“…The visualization of videos is gaining more attention [2,3] because of addressing challenges of data analysis arisen from video cameras contents [4][5][6]. Over the past decade, VV usefulness for traffic surveillance [7,8] application has been effectively demonstrated by researchers [9,10].…”

Section: Introductionmentioning

confidence: 99%

Glyph-based video visualization on Google Map for surveillance in smart cities

Mehboob

Abbas

Rehman

et al. 2017

J Image Video Proc.

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Video visualization (VV) is considered to be an essential part of multimedia visual analytics. Many challenges have arisen from the enormous video content of cameras which can be solved with the help of data analytics and hence gaining importance. However, the rapid advancement of digital technologies has resulted in an explosion of video data, which stimulates the needs for creating computer graphics and visualization from videos. Particularly, in the paradigm of smart cities, video surveillance as a widely applied technology can generate huge amount of videos from 24/7 surveillance. In this paper, a state of the art algorithm has been proposed for 3D conversion from traffic video content to Google Map. Time-stamped glyph-based visualization is used effectively in outdoor surveillance videos and can be used for event-aware detection. This form of traffic visualization can potentially reduce the data complexity, having holistic view from larger collection of videos. The efficacy of the proposed scheme has been shown by acquiring several unprocessed surveillance videos and by testing our algorithm on them without their pertaining field conditions. Experimental results show that the proposed visualization technique produces promising results and found effective in conveying meaningful information while alleviating the need of searching exhaustively colossal amount of video data.

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Section: Introductionmentioning

confidence: 99%

Glyph-based video visualization on Google Map for surveillance in smart cities

Mehboob

Abbas

Rehman

et al. 2017

J Image Video Proc.

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“…Security personnel can then browse the recorded faces and select a person. A description of the system's user interface is given in Girgensohn et al [1]. The interface shows where the selected person is walking on a floorplan (see Fig.…”

Section: Introductionmentioning

confidence: 99%

Robust People Detection and Tracking in a Multi-Camera Indoor Visual Surveillance System

Yang

Chen

Kimber

et al. 2007

Multimedia and Expo, 2007 IEEE International Conference On

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In this paper we describe the analysis component of an indoor, real-time, multi-camera surveillance system. The analysis includes: (1) a novel feature-level foreground segmentation method which achieves efficient and reliable segmentation results even under complex conditions, (2) an efficient greedy search based approach for tracking multiple people through occlusion, and (3) a method for multicamera handoff that associates individual trajectories in adjacent cameras. The analysis is used for an 18 camera surveillance system that has been running continuously in an indoor business over the past several months. Our experiments demonstrate that the processing method for people detection and tracking across multiple cameras is fast and robust.

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