Real-Time Tracking via On-line Boosting
Abstract
Very recently tracking was approached using classification techniques such as support vector machines. The object to be tracked is discriminated by a classifier from the background. In a similar spirit we propose a novel on-line AdaBoost feature selection algorithm for tracking. The distinct advantage of our method is its capability of on-line training. This allows to adapt the classifier while tracking the object. Therefore appearance changes of the object (e.g. out of plane rotations, illumination changes) are handled quite naturally. Moreover, depending on the background the algorithm selects the most discriminating features for tracking resulting in stable tracking results. By using fast computable features (e.g. Haar-like wavelets, orientation histograms, local binary patterns) the algorithm runs in real-time. We demonstrate the performance of the algorithm on several (publically available) video sequences. 1
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Recently, more tracking source codes have been made publicly available, e.g., the OAB, IVT, MIL, L1, and TLD algorithms, which have been commonly used for evaluation.
“Recently, more tracking source codes have been made publicly available, e.g., the OAB [22], IVT [47], MIL [5], L1 [40], and TLD [31] algorithms, which have been commonly used for evaluation.”
From Object Tracking Benchmark (2015) · cites this paper as [22]
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- Authors
- Grabner, H., Grabner, M., & Bischof, H.
- ORCID
- Bischof, H.
- Venue
- Procedings of the British Machine Vision Conference 2006
- Publisher
- British Machine Vision Association
- Published
- 2006
- Pages
- 6.1-6.10
- OpenAlex
- W2000326692
- Cited by
- 1,198
- References
- 26
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 7 · 2023: 22 · 2022: 31 · 2021: 47 · 2020: 63 · 2019: 100 · 2018: 109 · 2017: 127 · 2016: 147 · 2015: 143 · 2014: 144 · 2013: 97 · 2012: 58
- FWCI
- 9.90
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- British Machine Vision Conference (BMVC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- object tracking, feature selection, real-time tracking, background discrimination, appearance changes, orientation histograms, local binary pattern
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