A Metric for Performance Evaluation of Multi-Target Tracking Algorithms

Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estim...

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Main Authors: Ristic, B., Vo, Ba-Ngu, Clark, D., Vo, Ba Tuong
Format: Journal Article
Published: I E E E 2011
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/30433
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author Ristic, B.
Vo, Ba-Ngu
Clark, D.
Vo, Ba Tuong
author_facet Ristic, B.
Vo, Ba-Ngu
Clark, D.
Vo, Ba Tuong
author_sort Ristic, B.
building Curtin Institutional Repository
collection Online Access
description Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations.
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spelling curtin-20.500.11937-304332017-09-13T15:32:24Z A Metric for Performance Evaluation of Multi-Target Tracking Algorithms Ristic, B. Vo, Ba-Ngu Clark, D. Vo, Ba Tuong tracking Estimation performance evaluation Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations. 2011 Journal Article http://hdl.handle.net/20.500.11937/30433 10.1109/TSP.2011.2140111 I E E E restricted
spellingShingle tracking
Estimation
performance evaluation
Ristic, B.
Vo, Ba-Ngu
Clark, D.
Vo, Ba Tuong
A Metric for Performance Evaluation of Multi-Target Tracking Algorithms
title A Metric for Performance Evaluation of Multi-Target Tracking Algorithms
title_full A Metric for Performance Evaluation of Multi-Target Tracking Algorithms
title_fullStr A Metric for Performance Evaluation of Multi-Target Tracking Algorithms
title_full_unstemmed A Metric for Performance Evaluation of Multi-Target Tracking Algorithms
title_short A Metric for Performance Evaluation of Multi-Target Tracking Algorithms
title_sort metric for performance evaluation of multi-target tracking algorithms
topic tracking
Estimation
performance evaluation
url http://hdl.handle.net/20.500.11937/30433