The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Visual tracking with online Multiple Instance Learning
Abstract
In this paper, we address the problem of learning an adaptive appearance model for object tracking. In particular, a class of tracking techniques called “tracking by detection” have been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrades the classifier and can cause further drift. In this paper we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks. We present a novel online MIL algorithm for object tracking that achieves superior results with real-time performance.
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Cites
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Cited by
Online Object Tracking: A Benchmark
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Self-citationVisual object tracking using adaptive correlation filters
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A Benchmark and Simulator for UAV Tracking
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LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking
Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Bai, H., Xu, Y., Liao, C., & Ling, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00552 · Cited by 1,742
“We evaluate 35 algorithms on LaSOT to provide extensive baselines, comprising deep trackers ( e.g ., MDNet [42], TRACA [5], CFNet [50], SiamFC [4], StructSiam [59], DSiam [16], SINT [49] and VITAL [48]), correlation fil-ter trackers with hand-crafted features ( e.g ., ECO HC [7], DSST [8], CN [11], CSK [19], KCF [20],…”
BackgroundFast Online Object Tracking and Segmentation: A Unifying Approach
Wang, Q., Zhang, L., Bertinetto, L., Hu, W., & Torr, P. H. S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00142 · Cited by 1,554
Discriminative Scale Space Tracking
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Learning to Track at 100 FPS with Deep Regression Networks
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Robust object tracking via sparsity-based collaborative model
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Self-citation(2009) · doi:10.1109/iccv.2009.5459292 · Cited by 779
A survey of appearance models in visual object tracking
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Real-time part-based visual tracking via adaptive correlation filters
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LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
Fan, H., Bai, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Harshit, Huang, M., Liu, J., et al. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01387-y · Cited by 360
Joint Monocular 3D Vehicle Detection and Tracking
Hu, H.-N., Cai, Q.-Z., Wang, D., Lin, J., Sun, M., Kraehenbuehl, P., Darrell, T., & Yu, F. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00549 · Cited by 249
Partial Occlusion Handling for Visual Tracking via Robust Part Matching
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Visual Tracking via Probability Continuous Outlier Model
Wang, D., & Lu, H. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.445 · Cited by 113
A survey on online learning for visual tracking
Abbass, M. Y., Kwon, K.-C., Kim, N., Abdelwahab, S. A., El-Samie, F. E. A., & Khalaf, A. A. M. (2021) · The Visual Computer · doi:10.1007/s00371-020-01848-y · Cited by 64
Ship Detection and Tracking in Inland Waterways Using Improved YOLOv3 and Deep SORT
Jie, Y., Leonidas, L., Mumtaz, F., & Ali, M. (2021) · Symmetry · doi:10.3390/sym13020308 · Cited by 44
Metadata
- Authors
- Babenko, B., Yang, M.-H., & Belongie, S.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 983-990
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2167089254
- Cited by
- 1,770
- References
- 24
- FWCI
- 82.60
- Type
- conference paper
- Language
- en
- Open access
- closed
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