Integration of GIS and Moving Objects in Surveillance Video

Xie, Yujia; Wang, Meizhen; Liu, Xuejun; Wu, Yiguang

doi:10.3390/ijgi6040094

Cited by 20 publications

(16 citation statements)

References 33 publications

(37 reference statements)

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“…This work mainly discussed the integration of MCVO and GIS. The implementation of the integration was carried out on the basis of V-GIS fusion [9], V-GIS practical application [1,3], and our preliminary work on single camera video object and GIS integration [10]. The advantage of this integration was that it could achieve not only the extraction of video key information and spatial correlation visualization but also the spatially associated analysis of multi-camera video object, thereby assisting users to effectively monitor video operations.…”

Section: Resultsmentioning

confidence: 99%

“…This integration was essentially an augmented VR technique applied in GIS [9]. The integration of single camera video moving objects and GIS has been discussed before [10]. Practically, V-GIS is oriented toward multi-camera video information processing in a surveillance network, in which a single video moving object may appear in multiple camera fields of view, and its trajectory and semantic behavior have complex spatial-temporal associations related to multiple camera shots [11].…”

Section: Of 20mentioning

confidence: 99%

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Integration of Multi-Camera Video Moving Objects and GIS

Xie

Wang

Liu

et al. 2019

IJGI

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Of 20mentioning

confidence: 99%

Integration of Multi-Camera Video Moving Objects and GIS

Xie

Wang

Liu

et al. 2019

IJGI

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“…Calavia et al [12] proposed such a system to detect and identify abnormal and alarming situations by analyzing objects' trajectories. The integration of a video and GIS, on the other hand, aims to unify the representations of video information collected from different geographic locations [13]. As such, it does not conflict with the techniques of intelligent and context-aware video surveillance.…”

Section: Related Workmentioning

confidence: 99%

“…They proposed a hybrid NoSQL-SQL approach for real-time data access and structured on-the-fly analysis which can meet the requirements of increased spatio-temporal big data linking analysis. Trying to overcome the limitations of conventional video surveillance systems, such as the low efficiency in video searching, redundancy in video data transmission, and insufficient capability to position video content in geographic space, Xie et al [13] proposed the integration of a GIS and moving objects in surveillance videos by using motion detection and spatial mapping.…”

Section: Related Workmentioning

confidence: 99%

A Method for Estimating Surveillance Video Georeferences

Milosavljević¹,

Rančić²,

Dimitrijevic³

et al. 2017

IJGI

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Abstract:The integration of a surveillance camera video with a three-dimensional (3D) geographic information system (GIS) requires the georeferencing of that video. Since a video consists of separate frames, each frame must be georeferenced. To georeference a video frame, we rely on the information about the camera view at the moment that the frame was captured. A camera view in 3D space is completely determined by the camera position, orientation, and field-of-view. Since the accurate measuring of these parameters can be extremely difficult, in this paper we propose a method for their estimation based on matching video frame coordinates of certain point features with their 3D geographic locations. To obtain these coordinates, we rely on high-resolution orthophotos and digital elevation models (DEM) of the area of interest. Once an adequate number of points are matched, Levenberg-Marquardt iterative optimization is applied to find the most suitable video frame georeference, i.e., position and orientation of the camera.

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“…It has become an important data source in geographic information systems (GIS)-namely Video GIS [1][2][3][4][5][6]-providing an associative mechanism for the integrated analysis of mass video data and large-scale geographic scenes, and playing an important role in smart cities and urban security. With the widespread deployment of the GeoVideo sensor network and continuous technical advancements in video spatialization and video spatial information extraction (such as tracking the position and trajectory of moving objects, reconstructing 3D models, and so on) [7][8][9][10][11], the complexity and volume of GeoVideo data has increased dramatically.…”

Section: Introductionmentioning

confidence: 99%

Movement-Oriented Objectified Organization and Retrieval Approach for Heterogeneous GeoVideo Data

Zhu

Zhang

et al. 2018

IJGI

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Abstract:With the wide deployment of the video sensor network and the rapid development of video spatialization technology, the large volume of complex GeoVideo data necessitates improvements in the application efficiency of the GeoVideo database and GeoVideo surveillance system. Traditional storage management approaches focus on the optimization of access to the GeoVideo stream. However, they suffer from poor management of the diverse movement processes contained within it; for example, they cannot support associative queries or comprehensive analysis of multi-type GeoVideo data in complex geographic environments. This paper takes physical movement process in GeoVideo as a new type of object and carries out an objectified organization of the heterogeneous GeoVideo data around it (including the video stream, spatial references, interpretations of the video data, and the overall scene) in a Not only SQL-Structured Query Language (NoSQL-SQL) hybrid GeoVideo database. This paper systematically explores the hybrid spatiotemporal indexes and multi-modal retrieval methods around movement processes, which enrich the query modes of the GeoVideo data. A prototype implementation and experimental analysis are presented to prove the feasibility and effectiveness of this organization and retrieval approach.

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Integration of GIS and Moving Objects in Surveillance Video

Cited by 20 publications

References 33 publications

Integration of Multi-Camera Video Moving Objects and GIS

Integration of Multi-Camera Video Moving Objects and GIS

A Method for Estimating Surveillance Video Georeferences

Movement-Oriented Objectified Organization and Retrieval Approach for Heterogeneous GeoVideo Data

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