Performance Evaluation Methodology for Long-Term Single-Object Tracking
Autor: | Matej Kristan, Jiri Matas, Alan Lukezic, Luka Čehovin Zajc, Tomas Vojir |
---|---|
Rok vydání: | 2020 |
Předmět: |
BitTorrent tracker
business.industry Computer science 010401 analytical chemistry Video Recording 02 engineering and technology Temporal annotation Machine learning computer.software_genre 01 natural sciences 0104 chemical sciences Computer Science Applications Human-Computer Interaction Control and Systems Engineering Video tracking 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business computer Software Algorithms Information Systems |
Zdroj: | IEEE transactions on cybernetics. 51(12) |
ISSN: | 2168-2275 |
Popis: | A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition to maximize the analysis probing strength. The new measures outperform existing ones in interpretation potential and in better distinguishing between different tracking behaviors. We show that these measures generalize the short-term performance measures, thus linking the two tracking problems. Furthermore, the new measures are highly robust to temporal annotation sparsity and allow annotation of sequences hundreds of times longer than in the current datasets without increasing manual annotation labor. A new challenging dataset of carefully selected sequences with many target disappearances is proposed. A new tracking taxonomy is proposed to position trackers on the short-term/long-term spectrum. The benchmark contains an extensive evaluation of the largest number of long-term trackers and comparison to state-of-the-art short-term trackers. We analyze the influence of tracking architecture implementations to long-term performance and explore various redetection strategies as well as the influence of visual model update strategies to long-term tracking drift. The methodology is integrated in the VOT toolkit to automate experimental analysis and benchmarking and to facilitate the future development of long-term trackers. |
Databáze: | OpenAIRE |
Externí odkaz: |