A Novel Performance Evaluation Methodology for Single-Target Trackers
| Autor: | Ales Leonardis, Fatih Porikli, Matej Kristan, Gustavo Fernandez, Georg Nebehay, Roman Pflugfelder, Luka Cehovin, Tomas Vojir, Jiri Matas |
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| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Computer science BitTorrent tracker Computer Vision and Pattern Recognition (cs.CV) Applied Mathematics Computer Science - Computer Vision and Pattern Recognition 020207 software engineering 02 engineering and technology computer.software_genre Visualization Computational Theory and Mathematics Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Data mining Cluster analysis computer Equivalence (measure theory) Software |
| Zdroj: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 38:2137-2155 |
| ISSN: | 2160-9292 0162-8828 |
| Popis: | This paper addresses the problem of single-target tracker performance evaluation. We consider the performance measures, the dataset and the evaluation system to be the most important components of tracker evaluation and propose requirements for each of them. The requirements are the basis of a new evaluation methodology that aims at a simple and easily interpretable tracker comparison. The ranking-based methodology addresses tracker equivalence in terms of statistical significance and practical differences. A fully-annotated dataset with per-frame annotations with several visual attributes is introduced. The diversity of its visual properties is maximized in a novel way by clustering a large number of videos according to their visual attributes. This makes it the most sophistically constructed and annotated dataset to date. A multi-platform evaluation system allowing easy integration of third-party trackers is presented as well. The proposed evaluation methodology was tested on the VOT2014 challenge on the new dataset and 38 trackers, making it the largest benchmark to date. Most of the tested trackers are indeed state-of-the-art since they outperform the standard baselines, resulting in a highly-challenging benchmark. An exhaustive analysis of the dataset from the perspective of tracking difficulty is carried out. To facilitate tracker comparison a new performance visualization technique is proposed. Comment: Final version (Accepted), IEEE Pattern Analysis and Machine Intelligence, 2016 |
| Databáze: | OpenAIRE |
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