Viewable scene modeling for geospatial video search

Ay, Sakire Arslan; Zimmermann, Roger; Kim, Seon Ho

doi:10.1145/1459359.1459401

Cited by 82 publications

(52 citation statements)

References 12 publications

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“…Xiaotao Liu designed and implemented the automatic video annotation and querying system, named SEVA, with rich sensor information. The SEVA system filtered and refined the query results through the adjacent and location information recorded in video streams (Liu, Corner, & Shenoy, 2005 Kim, Ay, Yu, & Zimmermann, 2010;Ay, Zhang, Kim, He, & Zimmermann, 2009;Ay, Zimmermann, & Kim, 2008Ma, Ay, Zimmermann, & Kim, 2013). The Open Geospatial Consortium (OGC) defined the view cone model for the video frame.…”

Section: Video Retrieval Methods Based On Geographic Informationmentioning

confidence: 99%

Geographic Video 3d Data Model And Retrieval

Han

Cui

Kong

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: Geographic Video, Data Model, 3D, GIS, Data Retrieval ABSTRACT:Geographic video includes both spatial and temporal geographic features acquired through ground-based or non-ground-based cameras. With the popularity of video capture devices such as smartphones, the volume of user-generated geographic video clips has grown significantly and the trend of this growth is quickly accelerating. Such a massive and increasing volume poses a major challenge to efficient video management and query. Most of the today's video management and query techniques are based on signal level content extraction. They are not able to fully utilize the geographic information of the videos. This paper aimed to introduce a geographic video 3D data model based on spatial information. The main idea of the model is to utilize the location, trajectory and azimuth information acquired by sensors such as GPS receivers and 3D electronic compasses in conjunction with video contents. The raw spatial information is synthesized to point, line, polygon and solid according to the camcorder parameters such as focal length and angle of view. With the video segment and video frame, we defined the three categories geometry object using the geometry model of OGC Simple Features Specification for SQL. We can query video through computing the spatial relation between query objects and three categories geometry object such as VFLocation, VSTrajectory, VSFOView and VFFovCone etc. We designed the query methods using the structured query language (SQL) in detail. The experiment indicate that the model is a multiple objective, integration, loosely coupled, flexible and extensible data model for the management of geographic stereo video.

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Section: Video Retrieval Methods Based On Geographic Informationmentioning

confidence: 99%

Geographic Video 3d Data Model And Retrieval

Han

Cui

Kong

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Depending on the sensors that detected them, the positions can be either geometric (sequence of (x,y,t) triplets for 2D positions) [8] or symbolic (sequence of (rfidtag, t)) [9]. Also depending on the object's type and on the capability of the sensors associated to the object or embedded in the environment, additional data can be associated to the object's movement (e.g., for a mobile camera, it is interesting to capture information like orientation and field of view) [10].…”

Section: Hybrid Trajectory Based Query Definitionmentioning

confidence: 99%

Video Spatio-Temporal Filtering Based on Cameras and Target Objects Trajectories -- Videosurveillance Forensic Framework

Codreanu

Péninou

Sèdes

2015

2015 10th International Conference on Availability, Reliability and Security

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To cite this version:Dana Codreanu, André Péninou, Florence Sèdes. Video spatio-temporal filtering based on cameras and target objects trajectories -Videosurveillance forensic framework. 10 th International Conference on Availability, Reliability and Security (ARES 2015) Abstract-This paper presents our work about assisting videosurveillance agents in the search for particular video scenes of interest in transit network. This work has been developed based on requirements defined within different projects with the French National Police in a forensic goal. The video-surveillance agent inputs a query in the form of a hybrid trajectory (date, time, locations expressed with regards to different reference systems) and potentially some visual descriptions of the scene. The query processing starts with the interpretation of the hybrid trajectory and continues with a selection of a set of cameras likely to have filmed the spatial trajectory. The main contributions of this paper are: (1) a definition of the hybrid trajectory query concept, trajectory that is constituted of geometrical and symbolic segments represented with regards to different reference systems (e.g., geodesic system, road network), (2) a spatiotemporal filtering framework based on a spatio-temporal modeling of the transit network and associated cameras.

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“…We represent videos as a sequence of video frames, and each video frame is modeled as a Filed Of View (FOV) [3] as shown in Fig. 2.…”

Section: Video Spatial Model and Query Definitionsmentioning

confidence: 99%

“…Request permissions from Permissions@acm.org. tial properties at the fine granularity level of frame (e.g., Field-OfView [3]) . FOV model has been proven to be very useful for various media applications such as demonstrated by the online mobile media management system, MediaQ [6].…”

Section: Introductionmentioning

confidence: 99%

An efficient index structure for large-scale geo-tagged video databases

Lü

Shahabi

Kim

2014

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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An unprecedented number of user-generated videos (UGVs) are currently being collected by mobile devices, however, such unstructured data are very hard to index and search. Due to recent development, UGVs can be geo-tagged, e.g., GPS locations and compass directions, at the acquisition time at a very fine spatial granularity. Ideally, each video frame can be tagged by the spatial extent of its coverage area, termed Field-Of-View (FOV). In this paper, we focus on the challenges of spatial indexing and querying of FOVs in a large repository. Since FOVs contain both location and orientation information, and their distribution is non-uniform, conventional spatial indexes (e.g., R-tree, Grid) cannot index them efficiently. We propose a class of new R-tree-based index structures that effectively harness FOVs' camera locations, orientations and view-distances, in tandem, for both filtering and optimization. In addition, we present novel search strategies and algorithms for efficient range and directional queries on FOVs utilizing our indexes. Our experiments with a real-world dataset and a large synthetic video dataset (over 30 years worth of videos) demonstrate the scalability and efficiency of our proposed indexes and search algorithms and their superiority over the competitors.

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Viewable scene modeling for geospatial video search

Cited by 82 publications

References 12 publications

Geographic Video 3d Data Model And Retrieval

Geographic Video 3d Data Model And Retrieval

Video Spatio-Temporal Filtering Based on Cameras and Target Objects Trajectories -- Videosurveillance Forensic Framework

An efficient index structure for large-scale geo-tagged video databases

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