CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark
Autor: | Matej Kristan, Jani Käpylä, Jiri Matas, Alan Lukezic, Joni-Kristian Kamarainen, Ugur Kart, Ahmed Durmush |
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Přispěvatelé: | Tampere University, Research group: Multimedia Research Group - MRG, Computing Sciences, Research group: Vision |
Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition 020207 software engineering 02 engineering and technology 113 Computer and information sciences Tracking (particle physics) Object (computer science) Visualization Video tracking 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Clutter RGB color model 020201 artificial intelligence & image processing Computer vision Artificial intelligence business ComputingMethodologies_COMPUTERGRAPHICS |
Zdroj: | ICCV |
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 tackers 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 re-detection strategies as well as 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 future development of long-term trackers. |
Databáze: | OpenAIRE |
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