On the performance of the Box Particle Filter for extended object tracking using laser data

@article{Petrov2012OnTP,
  title={On the performance of the Box Particle Filter for extended object tracking using laser data},
  author={Nikolay Petrov and Martin Ulmke and Lyudmila S. Mihaylova and Amadou Gning and Marek Schikora and Monika Wieneke and Wolfgang Koch},
  journal={2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)},
  year={2012},
  pages={19-24},
  url={https://api.semanticscholar.org/CorpusID:17234611}
}
The performance of the recently proposed Box Particle Filter (Box PF) algorithm is evaluated utilising real measurements from laser range scanners obtained within a prototype security system replicating an airport corridor.
View on IEEE
eprints.lancs.ac.uk
6 Citations
Background Citations
1
Methods Citations
2
Results Citations
1

Figures from this paper

6 Citations

A Box Particle Filter Method for Tracking Multiple Extended Objects

For the first time, it is shown how interval-based approaches can deal efficiently with data association problems and reduce the computational complexity of the data association.

Extended Object Tracking: Introduction, Overview and Applications

An elaborate overview of current research in extended object tracking is provided, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.

Autonomous crowds tracking with box particle filtering and convolution particle filtering

Clustering based box-particle probability hypothesis density filtering

The proposed clustering based box-particle implementation of PHD filter can improve the performance of the tracking result, especially for the circumstances some of the targets are close to each other in the surveillance area.

Labeled box-particle CPHD filter for multiple extended targets tracking

The extended labeled box-particle cardinalized probability hypothesis density (ET-LBP-CPHD) is proposed, which relaxes the Poisson assumptions of the extended target probability hypothesis density (PHD) in target numbers, and propagates not only the intensity function but also cardinality distribution.

State of charge estimation of electrical vehicle battery by Using Box Particle Filter Signal Tracking Method

ABSTRACT This paper concentrates on the effective study of the online state of charge (SoC) estimation of lithium-ion batteries used in electric vehicles (EVs). Box Particle filters (BPF) are…

25 References

http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70031 Tracking Rectangular and Elliptical Extended Targets Using Laser Measurements

    Engineering, Physics

Extended Object Tracking Using Mixture Kalman Filtering

A solution to the extended object tracking problem by mixture Kalman filtering is proposed, formulated in a conditional dynamic linear (CDL) form and two latent indicator variables are proposed, characterising the mode of maneuvering and size type, respectively.

A Track Before Detect Algorithm for Tracking Extended Targets

An extended target track before detect algorithm is presented and the performance is compared to an algorithm based on the point target assumption.

Group and extended object tracking

The problem of tracking point targets moving in a group, or features of an extended object, is formulated via a general two component model via a Bayesian bootstrap filter to implement a nonlinear, multiple hypothesis, recursive estimator.

Extended object tracking based on combined set-theoretic and stochastic fusion

A novel combined stochastic and set-theoretic estimator that employs random hyperboloids to express the uncertainties about the true circular disc is derived.

Sequential Monte Carlo framework for extended object tracking

The authors present an alternative approach within the context of particle filtering, where they augment the state with the unknown association hypothesis, and sample candidate values from an efficiently designed proposal distribution, which elegantly captures the notion of a soft gating function.

Extended Object Tracking Using Monte Carlo Methods

This correspondence addresses the problem of tracking extended objects, such as ships or a convoy of vehicles moving in urban environment. Two Monte Carlo techniques for extended object tracking are…

Spatial distribution model for tracking extended objects

A Bayesian filter has been developed for tracking an extended object in clutter based on two simple axioms: the numbers of received target and clutter measurements in a frame are Poisson distributed and each target-related measurement is an independent 'random draw' from this spatial distribution.

International Conference on Information Fusion ( FUSION ) A Bayesian Approach to Extended Object Tracking and Tracking of Loosely Structured Target Groups

With simulated sensor data produced by a partly resolvable aircraft formation the addressed phenomena are illustrated and a Bayesian solution to the resulting tracking problem is proposed.

Particle Filtering Combined with Interval Methods for Tracking Applications

This chapter presents a new approach combining the Bayesian framework with interval methods, and a Box Particle Filter (Box-PF) is presented and its theoretical derivation is given based on a mixture of uniform probability density functions.