Online Object Tracking: A Benchmark
Abstract
Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with efforts on sharing code and datasets, it is of great importance to develop a library and benchmark to gauge the state of the art. After briefly reviewing recent advances of online object tracking, we carry out large scale experiments with various evaluation criteria to understand how these algorithms perform. The test image sequences are annotated with different attributes for performance evaluation and analysis. By analyzing quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.
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OTB-100 contains 100 different challenging videos.
“OTB-100 [33] contains 100 different challenging videos.”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [33]
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The Visual Object Tracking VOT2016 Challenge Results
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The Visual Object Tracking VOT2015 Challenge Results
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The Sixth Visual Object Tracking VOT2018 Challenge Results
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A Novel Performance Evaluation Methodology for Single-Target Trackers
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VITAL: VIsual Tracking via Adversarial Learning
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Self-citationRGB-T object tracking: Benchmark and baseline
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The Visual Object Tracking VOT2014 Challenge Results
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Self-citationAction-Decision Networks for Visual Tracking with Deep Reinforcement Learning
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Deep visual tracking: Review and experimental comparison
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Need for Speed: A Benchmark for Higher Frame Rate Object Tracking
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Multi-cue Correlation Filters for Robust Visual Tracking
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Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking
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Self-citationDiscriminative Correlation Filter Tracker with Channel and Spatial Reliability
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Deformable Siamese Attention Networks for Visual Object Tracking
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The Seventh Visual Object Tracking VOT2019 Challenge Results
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Learning Correspondence From the Cycle-Consistency of Time
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Transfer Learning Based Visual Tracking with Gaussian Processes Regression
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The Visual Object Tracking VOT2017 Challenge Results
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Self-citationMulti-task Correlation Particle Filter for Robust Object Tracking
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Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models
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Deep Learning for Visual Tracking: A Comprehensive Survey
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Real-time part-based visual tracking via adaptive correlation filters
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Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking
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Learning the Model Update for Siamese Trackers
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A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning
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In defense of color-based model-free tracking
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Understanding and Diagnosing Visual Tracking Systems
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Robust Visual Tracking via Convolutional Networks without Training
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Self-citationParallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking
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Sparse Modeling for Image and Vision Processing
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End-to-End Flow Correlation Tracking with Spatial-Temporal Attention
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Notice of Removal: The Visual Object Tracking VOT2013 Challenge Results
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Self-Supervised Deep Correlation Tracking
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VIFB: A Visible and Infrared Image Fusion Benchmark
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- Authors
- Wu, Y., Lim, J., & Yang, M.-H.
- Affiliations
- University of California, Merced (US)
- Hanyang University (KR)
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 2411-2418
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2089961441
- Semantic Scholar
- bfba194dfd9c7c27683082aa8331adc4c5963a0d
- Cited by
- 4,138
- References
- 68
- Citations by year
- 2026: 51 · 2025: 161 · 2024: 206 · 2023: 258 · 2022: 314 · 2021: 351 · 2020: 413 · 2019: 541 · 2018: 507 · 2017: 429 · 2016: 401 · 2015: 318 · 2014: 167 · 2013: 21
- FWCI
- 192.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- online object tracking, benchmark, evaluation criteria, robust tracking
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