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

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.

Spatiotemporal retrieval of dynamic video object trajectories in geographical scenes

“…These include one class as an R‐tree index based on the visual field (Wu et al, 2015), the determination of camera‐by‐camera topological relationships (Cho, Park, Kim, Lee, & Yoon, 2017), and the analysis of the field of view of the camera. Another method realizes the organization of multi‐camera video data by associating factors such as the moving object’s texture (Jian, Liao, Fan, & Xue, 2017), spatiotemporal behavior (Loy, Xiang, & Gong, 2010), and semantic aspects (Mehboob et al, 2017).…”

A Cloud-Based UTOPIA Smart Video Surveillance System for Smart Cities

“…Mehboob et al [12] propose an algorithm for 3D conversion from traffic video content to Google Map. Time-stamped glyph-based visualization is used in outdoor surveillance videos for the algorithm, which can be used for event-aware detection.…”

Integration of Multi-Camera Video Moving Objects and GIS

“…In some of these methods (e.g., view-based R-tree [3] and camera-based topology indexing [30]), video data organization is analyzed by examining the camera field of view. The other methods used moving object texture association [31], spatial-temporal behavior association [32], and semantic association [33]. A suitable mapping method must be selected to project the video on to the virtual scene model to integrate videos with geospatial information [34,35].…”

“…According to different mapping methods, the information fusion methods of surveillance video and virtual scene are divided into two categories: GIS-video image fusion (image projection) [37] and GIS-video moving object fusion (object projection) [38]. The implementation forms of GIS-video image fusion, including video image linked search analysis [4] and videos that are projected to the geographic scene [33], are easy to implement but lack the ability to analyze and understand video image contents. The object projection method extracts video semantic objects from the original video through object detection.…”