Literature DB >> 26812723

Visual Object Tracking Performance Measures Revisited.

Luka Čehovin, Aleš Leonardis, Matej Kristan.   

Abstract

The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments. This makes the cross-paper tracker comparison difficult. Furthermore, as some measures may be less effective than others, the tracking results may be skewed or biased toward particular tracking aspects. In this paper, we revisit the popular performance measures and tracker performance visualizations and analyze them theoretically and experimentally. We show that several measures are equivalent from the point of information they provide for tracker comparison and, crucially, that some are more brittle than the others. Based on our analysis, we narrow down the set of potential measures to only two complementary ones, describing accuracy and robustness, thus pushing toward homogenization of the tracker evaluation methodology. These two measures can be intuitively interpreted and visualized and have been employed by the recent visual object tracking challenges as the foundation for the evaluation methodology.

Year:  2016        PMID: 26812723     DOI: 10.1109/TIP.2016.2520370

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  5 in total

1.  Fast Object Tracking on a Many-Core Neural Network Chip.

Authors:  Lei Deng; Zhe Zou; Xin Ma; Ling Liang; Guanrui Wang; Xing Hu; Liu Liu; Jing Pei; Guoqi Li; Yuan Xie
Journal:  Front Neurosci       Date:  2018-11-16       Impact factor: 4.677

2.  Automated Intracellular Calcium Profiles Extraction from Endothelial Cells Using Digital Fluorescence Images.

Authors:  Marcial Sanchez-Tecuatl; Ajelet Vargaz-Guadarrama; Juan Manuel Ramirez-Cortes; Pilar Gomez-Gil; Francesco Moccia; Roberto Berra-Romani
Journal:  Int J Mol Sci       Date:  2018-11-02       Impact factor: 5.923

3.  Flipping food during grilling tasks, a dataset of utensils kinematics and dynamics, food pose and subject gaze.

Authors:  Débora Pereira; Yuri De Pra; Emidio Tiberi; Vito Monaco; Paolo Dario; Gastone Ciuti
Journal:  Sci Data       Date:  2022-01-12       Impact factor: 6.444

4.  Robust Visual Tracking Based on Adaptive Convolutional Features and Offline Siamese Tracker.

Authors:  Ximing Zhang; Mingang Wang
Journal:  Sensors (Basel)       Date:  2018-07-20       Impact factor: 3.576

5.  Global Motion-Aware Robust Visual Object Tracking for Electro Optical Targeting Systems.

Authors:  Byeong Hak Kim; Alan Lukezic; Jong Hyuk Lee; Ho Min Jung; Min Young Kim
Journal:  Sensors (Basel)       Date:  2020-01-20       Impact factor: 3.576

  5 in total

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