Graph Model Based Indoor Tracking

Jensen, Christian S.; Lu, Hua; Yang, Bin

doi:10.1109/mdm.2009.23

Cited by 133 publications

(100 citation statements)

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“…The movement track of an object (or raw movement, or raw trajectory) is defined basically as a sequence of spatio-temporal positions (i.e., positions that refer to the modifications of an object's spatial characteristics (coordinates and/or geometry) in time) [8]. 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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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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“…Meanwhile, different indoor tracking methods (Burgard et al, 1997;Girard et al, 2011;Jensen et al, 2009;Khider et al, 2012;Miller, 2006;Spassov, 2007;Thrun et al, 2005) design various strategies to avert deviation of tracks. However, they largely rely on positioning equipment, sensors and users.…”

Section: Introductionmentioning

confidence: 99%

“…mean velocity and velocity variance (Khider et al, 2012)). Jensen et al (Jensen et al, 2009) proposes a base graph model for tracking which represents the connectivity and accessibility of indoor space.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Spatial Model to Improve Indoor Tracking

Liu

Penard³

et al. 2015

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

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ABSTRACT:In this paper, we leverage spatial model to process indoor localization results and then improve the track consisting of measured locations. We elaborate different parts of spatial model such as geometry, topology and semantics, and then present how they contribute to the processing of indoor tracks. The initial results of our experiment reveal that spatial model can support us to overcome problems such as tracks intersecting with obstacles and unstable shifts between two location measurements. In the future, we will investigate more exceptions of indoor tracking results and then develop additional spatial methods to reduce errors of indoor tracks.

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“…A recent boost in technological advancements for tracking people in indoor environments has led to increasing possibilities for the development of indoor navigational models (Mautz et al 2010). Alternatively, several researchers have developed a wide variety of indoor navigational models ranging from abstract space models (Becker et al 2009) and 3D models (Coors 2003, Li & He 2008 to pure network models (Jensen et al 2009, Karas et al 2006, Lee 2001, Lee 2004. While these models might be useful in specific situations, a general framework for indoor navigation modelling has still to reach full maturity (Nagel et al 2010).…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

Calculating Least Risk Paths in 3d Indoor Space

Vanclooster

Maeyer

Fack

et al. 2013

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

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ABSTRACT:Over the last couple of years, research on indoor environments has gained a fresh impetus; more specifically applications that support navigation and wayfinding have become one of the booming industries. Indoor navigation research currently covers the technological aspect of indoor positioning and the modelling of indoor space. The algorithmic development to support navigation has so far been left mostly untouched, as most applications mainly rely on adapting Dijkstra's shortest path algorithm to an indoor network. However, alternative algorithms for outdoor navigation have been proposed adding a more cognitive notion to the calculated paths and as such adhering to the natural wayfinding behaviour (e.g. simplest paths, least risk paths). These algorithms are currently restricted to outdoor applications. The need for indoor cognitive algorithms is highlighted by a more challenged navigation and orientation due to the specific indoor structure (e.g. fragmentation, less visibility, confined areas…). As such, the clarity and easiness of route instructions is of paramount importance when distributing indoor routes. A shortest or fastest path indoors not necessarily aligns with the cognitive mapping of the building. Therefore, the aim of this research is to extend those richer cognitive algorithms to three-dimensional indoor environments. More specifically for this paper, we will focus on the application of the least risk path algorithm of Grum (2005) to an indoor space. The algorithm as proposed by Grum (2005) is duplicated and tested in a complex multi-storey building. The results of several least risk path calculations are compared to the shortest paths in indoor environments in terms of total length, improvement in route description complexity and number of turns. Several scenarios are tested in this comparison: paths covering a single floor, paths crossing several building wings and/or floors. Adjustments to the algorithm are proposed to be more aligned to the specific structure of indoor environments (e.g. no turn restrictions, restricted usage of rooms, vertical movement) and common wayfinding strategies indoors. In a later stage, other cognitive algorithms will be implemented and tested in both an indoor and combined indoor-outdoor setting, in an effort to improve the overall user experience during navigation in indoor environments. * Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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Graph Model Based Indoor Tracking

Cited by 133 publications

References 10 publications

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

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

Leveraging Spatial Model to Improve Indoor Tracking

Calculating Least Risk Paths in 3d Indoor Space

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