Road Map Extraction using GMTI Tracking

Ulmke, Martin; Koch, Wolfgang

doi:10.1109/icif.2006.301564

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…a road map. After a suitable post-processing, the produced tracks of motion-constrained targets simply define the corresponding constraints and can thus be extracted from tracking-based results [15,16]. A similar discussion for wide-area maritime surveillance using AIS transponder data can be found in [17], AIS: Automatic Identification System.…”

Section: Tracking-derived Regularity Patternsmentioning

confidence: 99%

Perspectives for a backbone technology: tracking in real-world applications

Koch

2010

IEEE Aerosp. Electron. Syst. Mag.

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Over four decades, target tracking bas evolved into a mature discipline acting as a backbone for emerging engineering applications. Illustrated by examples, we discuss along wbicb lines tracking is likely to evolve into a key tecbnology enabling design and development of real-world surveillance and control systems. Four aspects are of particular importance: 1) extraction of bigber JDL level information on target cbaracteristics from precision tracks, 2) tracking-derived anomaly detection, 3) exploitation of attribute information from poor resolution sensors over a time window, 4) in-deptb integration of context information enabling information extraction even from unwanted propagation pbenomena.

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Section: Tracking-derived Regularity Patternsmentioning

confidence: 99%

Perspectives for a backbone technology: tracking in real-world applications

Koch

2010

IEEE Aerosp. Electron. Syst. Mag.

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“…From refs. [ 37 , 45 ] we can see that a given road through a real road network is described by nodes and edges in digital roadmaps, where nodes indicate that a road changes quality and edges represent individual road segments. The process of using roadmap information to assist ground target tracking can be divided into two steps.…”

Section: Gmphdf Incorporating Map Informationmentioning

confidence: 99%

Tracking Ground Targets with a Road Constraint Using a GMPHD Filter

Zheng

Gao

2018

Sensors

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The Gaussian mixture probability hypothesis density (GMPHD) filter is applied to the problem of tracking ground moving targets in clutter due to its excellent multitarget tracking performance, such as avoiding measurement-to-track association, and its easy implementation. For the existing GMPHD-based ground target tracking algorithm (the GMPHD filter incorporating map information using a coordinate transforming method, CT-GMPHD), the predicted probability density of its target state is given in road coordinates, while its target state update needs to be performed in Cartesian ground coordinates. Although the algorithm can improve the filtering performance to a certain extent, the coordinate transformation process increases the complexity of the algorithm and reduces its computational efficiency. To address this issue, this paper proposes two non-coordinate transformation roadmap fusion algorithms: directional process noise fusion (DNP-GMPHD) and state constraint fusion (SC-GMPHD). The simulation results show that, compared with the existing algorithms, the two proposed roadmap fusion algorithms are more accurate and efficient for target estimation performance on straight and curved roads in a cluttered environment. The proposed methods are additionally applied using a cardinalized PHD (CPHD) filter and a labeled multi-Bernoulli (LMB) filter. It is found that the PHD filter performs less well than the CPHD and LMB filters, but that it is also computationally cheaper.

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“…Most of the state-of-the-art methods, which exploit context information, are strongly correlated to a particular detection method. For instance, road detection approaches [15], [2], [16], [5], are used to provide key information for driving assistance applications or to define regions of interest for object tracking [2], [11]. In contrast, this study is inspired by [9], [8], [13], [11] and aims to use extracted context (road) information to directly improve the quality of multi-object tracking.…”

Section: I-b Contributionmentioning

confidence: 99%

Context-based vector fields for multi-object tracking in application to road traffic

Sattarov

Rodriguez

Gepperth

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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In this contribution, we propose to use road and lane information as contextual cues in order to increase the precision of multi-object object tracking. For tracking, we employ a Monte Carlo implementation of a Probability Hypothesis Density (PHD)-filter, whereas scene context (road and lane information) is taken from annotated street maps. The novel aspect of the presented work is the tightly coupling of context information and the particle filtering process. This is achieved by injecting a priori particles representing locally expected motions, which are in turn determined by the local road and the lane configuration. This approach is evaluated on objects (tracklets) from the public KITTI benchmark database. Our experimental findings demonstrate a considerable tracking precision increasing when including this kind of a priori knowledge. At the same time, the approach is able to determine objects whose movements differ from the locally expected motion, which is an important feature for safety applications.

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Road Map Extraction using GMTI Tracking

Cited by 8 publications

References 13 publications

Perspectives for a backbone technology: tracking in real-world applications

Perspectives for a backbone technology: tracking in real-world applications

Tracking Ground Targets with a Road Constraint Using a GMPHD Filter

Context-based vector fields for multi-object tracking in application to road traffic

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