Performance evaluation for tracking algorithms using object labels

Popoola, Jide Julius; Amer, A.

doi:10.1109/icassp.2008.4517714

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…Most papers in their experimental evaluation use a limited number of videos. For example, only six videos are being used in [24]. And, the often used BoBoT dataset, tested in [25], consists of ten different video sequences.…”

Section: Data For Tracker Evaluationmentioning

confidence: 99%

“…The Performance Evaluation of Tracking and Surveillance, PETS, workshop series was one of the first to evaluate trackers with ground truth, proposing performance measures for comparing tracking algorithms. Other performance measures for tracking are proposed by [38] and [24], as well as in [36]. In the more recent PETS series, [29], VACE [39], and CLEAR [40] metrics were developed for evaluating the performance of multiple target detection and tracking, while in case of single object tracking evaluation there is no consensus and many variations of the same measures are being proposed.…”

Section: Tracking Evaluation Measuresmentioning

confidence: 99%

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Visual Tracking: An Experimental Survey

2014

IEEE Trans. Pattern Anal. Mach. Intell.

1,178

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There is a large variety of trackers, which have been proposed in the literature during the last two decades with some mixed success. Object tracking in realistic scenarios is a difficult problem, therefore, it remains a most active area of research in computer vision. A good tracker should perform well in a large number of videos involving illumination changes, occlusion, clutter, camera motion, low contrast, specularities, and at least six more aspects. However, the performance of proposed trackers have been evaluated typically on less than ten videos, or on the special purpose datasets. In this paper, we aim to evaluate trackers systematically and experimentally on 315 video fragments covering above aspects. We selected a set of nineteen trackers to include a wide variety of algorithms often cited in literature, supplemented with trackers appearing in 2010 and 2011 for which the code was publicly available. We demonstrate that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing. We find that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score. The analysis under a large variety of circumstances provides objective insight into the strengths and weaknesses of trackers.

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Section: Data For Tracker Evaluationmentioning

confidence: 99%

Section: Tracking Evaluation Measuresmentioning

confidence: 99%

Visual Tracking: An Experimental Survey

2014

IEEE Trans. Pattern Anal. Mach. Intell.

1,178

View full text Add to dashboard Cite

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“…Examples include the framework introduced by PETS (Performance Evaluation of Tracking and Surveillance), ETISEO (Evaluation du Traitement et de l'Interpretation de Sequences vidEO), CAVIAR (Context Aware Vision using Imagebased Active Recognition), CLEAR (Classification of Events, Activities and Relationships). Other smaller-scale evaluation frameworks include comprehensive proposals such as the one in [1], and simple approaches such as the one based on "pseudo-synthetic video" sequences [2], on frame-based and object-based metrics [3], on the Label and Size Based Evaluation Measure (LSBEM) [4], or on measuring the tracking difficulty using a reflective model [5]. None of these frameworks has yet been widely taken up by the research This work was supported in part by the EU, under the FP7 project APIDIS (ICT-216023).…”

Section: Introductionmentioning

confidence: 99%

PFT: A protocol for evaluating video trackers

Nawaz

Cavallaro

2011

2011 18th IEEE International Conference on Image Processing

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The growing interest in developing video tracking algorithms has not been accompanied by the development of commonly used evaluation criteria to assess and to compare their performance. Researchers often present trackers' results on different datasets and evaluate them with different performance measures thus hindering both formative and summative quality assessment. In this paper, we present a protocol to evaluate the performance of tracking algorithms that tests video trackers using a set of trials and a pre-defined set of sequences and that enables objective and reproducible performance evaluation of trackers using ground truth information. Each trial highlights strengths and weaknesses of a tracker on simulated test scenarios on real sequences that represent real-world scenarios. Moreover a new evaluation measure is introduced that allows us to summarize the performance of a tracker based on the lost-track-ratio curve. The validation and the effectiveness of the proposed protocol is demonstrated experimentally on three trackers and its implementation is made available online to the research community.

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Some Cases of Object Tracking Parametrs Optimization in Video Stream

Kornachuk

Egorov

2021

2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

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Performance evaluation for tracking algorithms using object labels

Cited by 7 publications

References 3 publications

Visual Tracking: An Experimental Survey

Visual Tracking: An Experimental Survey

PFT: A protocol for evaluating video trackers

Some Cases of Object Tracking Parametrs Optimization in Video Stream

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