Integrating Multimedia GIS Technologies in a Recommendation System for Geotourism

et al. 2021

Transactions in GIS

Video synopsis offers the possibility to compress long‐term video objects into a short‐term playback. This is beneficial for the quick retrieval and expression of numerous video objects in a virtual geographic environment. However, existing methods do not consider the spatiotemporal relationships among video objects in different cameras. To overcome this problem, we propose optimal virtual viewpoint‐based multi‐camera video synopsis, which involves: locating the camera position and the field of view; constructing a video image observability model to enumerate the observable camera combinations; constructing an evaluation model to optimally select the observable combinations and obtain the virtual viewpoint group; and setting the parameters of the video object display to obtain a multi‐camera video synopsis of a geographic scene. Experimental results showed that our method achieved the rapid display of global motion across cameras with numerous video objects, and always outperformed separate camera‐based video synopsis expression.

Section: Related Workmentioning

confidence: 99%

Multi‐camera video synopsis of a geographic scene based on optimal virtual viewpoint

et al. 2021

Transactions in GIS

“…Typical camera spatialization models are the quadrilateral model of a 2D plane (Walton, Berger, Ebert, & Chen, 2014), the pyramid model in a 3D scene (Du, Bista, & Varshney, 2016), and the coverage analysis model based on camera grids (Wang, Liu, Zhang, & Wang, 2017). Based on the concepts of multimedia GIS (Charou, Kabassi, Martinis, & Stefouli, 2010), geographic video (geo‐video) (McDermid, Franklin, & LeDrew, 2005), and video GIS (Navarrete & Blat, 2002), data organization methods such as metadata description (Han, Kong, Qin, & Wang, 2013) and global positioning system (GPS) association (Feng & Song, 2014) were constructed in early research. These methods could perform the geographic retrieval and display video images by describing the corresponding relationship between the video frames and geographical locations.…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal retrieval of dynamic video object trajectories in geographical scenes

et al. 2020

Transactions in GIS

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.

“…In the area of V-GIS integrated data organization, early researchers constructed prototype systems such as multimedia GIS [24], geo-video [25], and video GIS [26] by describing the correspondence between video frames and geographic locations. These studies implemented geographic video image retrieval.…”

Section: Related Workmentioning

confidence: 99%

Integration of Multi-Camera Video Moving Objects and GIS

et al. 2019

IJGI

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.