Multiple Extended Target Tracking With Labeled Random Finite Sets

Beard, Michael; Reuter, Stephan; Granström, Karl; Vo, Ba-Tuong; Vo, Ba-Ngu; Scheel, Alexander

doi:10.1109/tsp.2015.2505683

Cited by 191 publications

(181 citation statements)

References 28 publications

Supporting

Mentioning

166

Contrasting

Order By: Relevance

“…Hence, they are distributed according to the Poisson RFS [39] g C with intensity function κ(z) = λ C p C (z). Using these assumptions, the likelihood of obtaining a set of measurements from a given multi-object state is [37]…”

Section: Multi-object Likelihood Functionmentioning

confidence: 99%

Tracking Multiple Vehicles Using a Variational Radar Model

Scheel

Dietmayer

2019

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

High-resolution radar sensors are able to resolve multiple detections per object and therefore provide valuable information for vehicle environment perception. For instance, multiple detections allow to infer the size of an object or to more precisely measure the object's motion. Yet, the increased amount of data raises the demands on tracking modules: measurement models that are able to process multiple detections for an object are necessary and measurement-to-object associations become more complex. This paper presents a new variational radar model for tracking vehicles using radar detections and demonstrates how this model can be incorporated into a Random-Finite-Setbased multi-object filter. The measurement model is learned from actual data using variational Gaussian mixtures and avoids excessive manual engineering. In combination with the multiobject tracker, the entire process chain from the raw measurements to the resulting tracks is formulated probabilistically. The presented approach is evaluated on experimental data and it is demonstrated that the data-driven measurement model outperforms a manually designed model.

show abstract

Section: Multi-object Likelihood Functionmentioning

confidence: 99%

Tracking Multiple Vehicles Using a Variational Radar Model

Scheel

Dietmayer

2019

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hence, scenarios involving, for example, extended concave targets [76] might still cause confusion. Although ad-hoc solutions that take into consideration the expected size and concavity of the targets could be employed to overcome this limitation, a more principled solution would be to utilize RFS-based extended target tracking approaches such as proposed in [77] while taking into account the expected distribution of the extended targets in the computation of the ILH.…”

Section: Resultsmentioning

confidence: 99%

Image-Based Multi-Target Tracking Through Multi-Bernoulli Filtering with Interactive Likelihoods

Hoak¹,

Medeiros²,

Povinelli³

2017

Preprint

View full text Add to dashboard Cite

Abstract:We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

show abstract

“…Hence, scenarios involving, for example, extended concave targets [77] might still cause confusion. Although ad hoc solutions that take into consideration the expected size and concavity of the targets could be employed to overcome this limitation, a more principled solution would be to utilize RFS-based extended target tracking approaches, such as proposed in [78], while taking into account the expected distribution of the extended targets in the computation of the ILH. …”

Section: Discussionmentioning

confidence: 99%

Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods

Hoak

Medeiros

Povinelli

2017

Sensors

View full text Add to dashboard Cite

We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

show abstract

Multiple Extended Target Tracking With Labeled Random Finite Sets

Cited by 191 publications

References 28 publications

Tracking Multiple Vehicles Using a Variational Radar Model

Tracking Multiple Vehicles Using a Variational Radar Model

Image-Based Multi-Target Tracking Through Multi-Bernoulli Filtering with Interactive Likelihoods

Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods

Contact Info

Product

Resources

About