Abstract
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.
Highlights
Intelligent surveillance in smart cities has rapidly progressed in last 10 years and has intended to provide situational awareness and semantic information for proactive and predictive management of smart cities with better understanding of the environmental activity [1]
Video visualization (VV) usefulness for traffic surveillance [7, 8] application has been effectively demonstrated by researchers [9, 10]
VV offers Spatio-temporal summary and overview of large collection of videos, and its abstract representation of meaningful information assists the users in video content [9, 11]
Summary
Intelligent surveillance in smart cities has rapidly progressed in last 10 years and has intended to provide situational awareness and semantic information for proactive and predictive management of smart cities with better understanding of the environmental activity [1]. VV illustrates the joint process of video analysis and subsequent derivation of representative presentation of essence of visual contents [2]. The visualization of videos is gaining more attention [2, 3] because of addressing challenges of data analysis arisen from video cameras contents [4–6]. VV offers Spatio-temporal summary and overview of large collection of videos, and its abstract representation of meaningful information assists the users in video content [9, 11]. Conventional techniques of visual representation such as time series plot have difficulties in conveying impressions from large video collection [9]
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