Performance Evaluation Methodology for Long-Term Single-Object Tracking

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00354239" target="_blank" >RIV/68407700:21230/21:00354239 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/TCYB.2020.2980618" target="_blank" >https://doi.org/10.1109/TCYB.2020.2980618</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TCYB.2020.2980618" target="_blank" >10.1109/TCYB.2020.2980618</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Performance Evaluation Methodology for Long-Term Single-Object Tracking

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Performance Evaluation Methodology for Long-Term Single-Object Tracking

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA18-05360S" target="_blank" >GA18-05360S: Řešení inverzních problémů vznikajících při analýze rychle se pohybujících objektů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Cybernetics

ISSN

2168-2267

e-ISSN

2168-2275

Svazek periodika

51

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

6305-6318

Kód UT WoS článku

000733232400060

EID výsledku v databázi Scopus

2-s2.0-85122211177