Performance Evaluation Metrics and Statistics for Positional Tracker Evaluation

Needham, Chris J.; Boyle, Roger D.

doi:10.1007/3-540-36592-3_27

Cited by 59 publications

(48 citation statements)

References 2 publications

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“…Objective evaluation measures have attracted a considerable interest [33][34][35][36][37][38][39] and are used for the final assessment of an algorithm as well as for the development process to compare different parameter sets of an algorithm on large datasets.…”

Section: Objective Evaluationmentioning

confidence: 99%

Accurate appearance-based Bayesian tracking for maneuvering targets

Maggio

Cavallaro

2009

Computer Vision and Image Understanding

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We propose a tracking algorithm that combines the Mean Shift search in a Particle Filtering framework and a target representation that uses multiple semi-overlapping color histograms. The target representation introduces spatial information that accounts for rotation and anisotropic scaling without compromising the flexibility typical of color histograms. Moreover, the proposed tracker can generate a smaller number of samples than Particle Filter as it increases the particle efficiency by moving the samples toward close local maxima of the likelihood using Mean Shift. Experimental results show that the proposed representation improves the robustness to clutter and that, especially on highly maneuvering targets, the combined tracker outperforms Particle Filter and Mean Shift in terms of accuracy in estimating the target size and position while generating only 25% of the samples used by Particle Filter.

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Section: Objective Evaluationmentioning

confidence: 99%

Accurate appearance-based Bayesian tracking for maneuvering targets

Maggio

Cavallaro

2009

Computer Vision and Image Understanding

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“…A measure of performance evaluation of these algorithms is necessary to quantify how reliable a tracking algorithm is in a particular scene. Many types of metrics have been proposed [4,6] and defined to address this issue but most of them are dependent on ground truth data in order to compare tracking results. In [4], the authors define three different metrics.…”

Section: Introductionmentioning

confidence: 99%

“…The last metric computes the number of reference object Ids per detected object. In [6], the performance of a tracking algorithm is based on the distance of tracked points and their corresponding true target positions. The authors also compute the area between the ground truth and detected trajectories.…”

Section: Introductionmentioning

confidence: 99%

Online evaluation of tracking algorithm performance

Chau

Brémond

Thonnat

2009

3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009)

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This paper presents a method to evaluate online the performance of tracking algorithms in surveillance videos. We use a set of features to compute the confidence of trajectories and also the precision of tracking results. A global score is computed online based on these features and is used to estimate the performance of tracking algorithms. The method has been tested with two real video sequences and two tracking algorithms. The similar variations between the results obtained by the proposed method and the output of a supervised evaluation tool using ground truth data have showed the performance of our global score. The advantages of our approach over the existing state of the art approaches are: (i) few a priori knowledge information is required, (ii) the method can be applied in complex scenes containing several mobile objects and (iii) we can simultaneously compare the performance of different tracking algorithms.

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“…The natural way to analyze the accuracy of any tracking system is to compare it to some reliable reference data. While a selection of comparison methods is readily available to the research community (Needham & Boyle, 2003), a reliable reference data (ground truth) can be hard to obtain, especially if greater accuracy is desired. Publicly available collections of video recordings with registered 3D ground truth information can be helpful, but are very scarce and with limited selection (Scharstein & Szeliski, 2003;CVTI, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…(Christensen & Förstner, 1997;Gavrila, 1999;Black et al, 2003;Bashir & Porikli, 2006;Georis et al, 2003). In (Needham & Boyle, 2003), several metrics are presented for comparing the tracked trajectories, but they are still limited to 2D. The paper also describes an example of how to generate ground truth data by manually marking the video sequence.…”

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confidence: 99%

Analysis of Video-Based 3D Tracking Accuracy by Using Electromagnetic Tracker as a Reference

Zazula¹

2007

Scene Reconstruction Pose Estimation and Tracking

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Performance Evaluation Metrics and Statistics for Positional Tracker Evaluation

Cited by 59 publications

References 2 publications

Accurate appearance-based Bayesian tracking for maneuvering targets

Accurate appearance-based Bayesian tracking for maneuvering targets

Online evaluation of tracking algorithm performance

Analysis of Video-Based 3D Tracking Accuracy by Using Electromagnetic Tracker as a Reference

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