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 a… Show more
“…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).…”
Current studies on video trajectory retrieval focus on the retrieval and analysis of image content, neglecting the gap between the spatiotemporal continuity of retrieval conditions and the spatiotemporal discontinuity of multi‐camera video trajectories. In this study, we propose a method for the spatiotemporal retrieval of dynamic video object trajectories in geographic scenes. Based on the camera calibration, the proposed method organizes the scene, cameras, and trajectories, constructs the spatiotemporal constraints, and queries the trajectories using two measures: camera‐by‐camera retrieval and global trajectory retrieval. The proposed method was verified through experiments, and the results demonstrate that both measures can query trajectories effectively and reduce the spatiotemporal video review range under different spatiotemporal constraints. Furthermore, compared with camera‐by‐camera retrieval, global trajectory retrieval can reduce the spatiotemporal video review range further and return more accurate results. The proposed method may provide support for the spatial analysis and understanding of surveillance video data.
“…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).…”
Current studies on video trajectory retrieval focus on the retrieval and analysis of image content, neglecting the gap between the spatiotemporal continuity of retrieval conditions and the spatiotemporal discontinuity of multi‐camera video trajectories. In this study, we propose a method for the spatiotemporal retrieval of dynamic video object trajectories in geographic scenes. Based on the camera calibration, the proposed method organizes the scene, cameras, and trajectories, constructs the spatiotemporal constraints, and queries the trajectories using two measures: camera‐by‐camera retrieval and global trajectory retrieval. The proposed method was verified through experiments, and the results demonstrate that both measures can query trajectories effectively and reduce the spatiotemporal video review range under different spatiotemporal constraints. Furthermore, compared with camera‐by‐camera retrieval, global trajectory retrieval can reduce the spatiotemporal video review range further and return more accurate results. The proposed method may provide support for the spatial analysis and understanding of surveillance video data.
“…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.…”
A smart city is a future city that enables citizens to enjoy Information and Communication Technology (ICT) based smart services with any device, anytime, anywhere. It heavily utilizes Internet of Things. It includes many video cameras to provide various kinds of services for smart cities. Video cameras continuously feed big video data to the smart city system, and smart cities need to process the big video data as fast as it can. This is a very challenging task because big computational power is required to shorten processing time. This paper introduces UTOPIA Smart Video Surveillance, which analyzes the big video images using MapReduce, for smart cities. We implemented the smart video surveillance in our middleware platform. This paper explains its mechanism, implementation, and operation and presents performance evaluation results to confirm that the system worked well and is scalable, efficient, reliable, and flexible.
“…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].…”
Section: Related Workmentioning
confidence: 99%
“…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.…”
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.