Evaluation of tracking reliability metrics based on information theory and normalized correlation

Loutas, E.; Nikolaidis, Nikos; Pitas, I.

doi:10.1109/icpr.2004.1333857

Cited by 9 publications

(8 citation statements)

References 12 publications

(17 reference statements)

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“…There have been several previously defined informationtheoretic metrics including [17,27,21,16,24,13]. However, these metrics do not handle all of the scenarios involved in spatio-temporal tracking and do not include the list of properties shown in Table 7.…”

Section: Comparison To Previously Defined Information-theoretic Metricsmentioning

confidence: 99%

Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence

Adams¹

2019

2019 IEEE Winter Applications of Computer Vision Workshops (WACVW)

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A unified metric is given for the evaluation of object tracking systems. The metric is inspired by KL-divergence or relative entropy, which is commonly used to evaluate clustering techniques. Since tracking problems are fundamentally different from clustering, the components of KL-divergence are recast to handle various types of tracking errors (i.e., false alarms, missed detections, merges, splits). Scoring results are given on a standard tracking dataset (Oxford Town Centre Dataset), as well as several simulated scenarios. Also, this new metric is compared with several other metrics including the commonly used Multiple Object Tracking Accuracy metric. In the final section, advantages of this metric are given including the fact that it is continuous, parameter-less and comprehensive.

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Section: Comparison To Previously Defined Information-theoretic Metricsmentioning

confidence: 99%

Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence

Adams¹

2019

2019 IEEE Winter Applications of Computer Vision Workshops (WACVW)

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“…As stated in Section 2 the tracker uses conditional linear Gaussian models for the density functions. For comparison of two Gaussian density the Kullback Leibler divergence is of great interest [10] [3]. For the sake of speed Mahalanobis could be a good measurement but it will not provide an error measurement when the two Gaussian distributions are having the same mean, albeit with different covariances.…”

Section: Online Evaluationmentioning

confidence: 99%

Online failure detection and correction for Bayesian sparse feature-based object tracking

Biresaw

Alvarez

Regazzoni

2011

2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Online evaluation of tracking algorithms is an important task in real time tracking systems to detect failures. In visual object tracking based on sparse features, detecting the failure of one of the feature points (corners) and correcting it will improve the performance of the tracker as a whole. In this paper a time reversed Markov chain is applied as evaluation technique to identify the failed trackers and Partial Least Square regression is used for learning the correlation between feature points from training data set. The detected feature point trackers are recovered from the knowledge of the learned correlation model. The results are explained on a Bayesian algorithm for rigid/nonrigid 2D visual object tracking. The experimental outcomes show a global performance improvement of the tracking algorithm even in the presence of clutter.

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“…In the next section, a single metric based on information theory will be described which captures all the aforementioned track pathologies and provides a comprehensive overall performance measure. This work is not to be confused with Loutas' appearance-based information theoretic tracker evaluation approach in [10] where comparison is made between the object reference chip and the track chip. This paper takes a different approach by measuring directly the similarity between the truth and the observed track states.…”

Section: Existing Tracking Metricsmentioning

confidence: 99%

An information theoretic approach for tracker performance evaluation

Edward

Matthew

Michael

2009

2009 IEEE 12th International Conference on Computer Vision

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Automated tracking of vehicles and people is essential for the effective utilization of imagery in wide area surveillance applications. In order to determine the best tracking algorithm and parameters for a given application, a comprehensive evaluation procedure is required. However, despite half a century of research in multi-target tracking, there is no consensus on how to score the overall performance of these trackers. Existing evaluation approaches assess tracker performance through measures of correspondence between ground truth tracks and system tracks using metrics such as track detection rate, track completeness, track fragmentation rate, and track ID change rate. However, each of these only provides a partial measure of performance and no good method exists to combine them into a holistic metric. Towards this end, this paper presents a pair of information theoretic metrics with similar behavior to the Receiver Operating Characteristic (ROC) curves of signal detection theory. Overall performance is evaluated with the percentage of truth information that a tracker captured and the total amount of false information that it reported. Information content is quantified through conditional entropy and mutual information computations using numerical estimates of the probability of association between the truth and the system tracks. This paper demonstrates how these information quality metrics provide a comprehensive evaluation of overall tracker performance and how they can be used to perform tracker comparisons and parameter tuning on wide-area surveillance imagery and other applications. 1

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Evaluation of tracking reliability metrics based on information theory and normalized correlation

Abstract: ABSTRACT

Cited by 9 publications

References 12 publications

Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence

Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence

Online failure detection and correction for Bayesian sparse feature-based object tracking

An information theoretic approach for tracker performance evaluation

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