The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Real-time visual tracking using compressive sensing
Abstract
The ℓ1tracker obtains robustness by seeking a sparse representation of the tracking object via ℓ1norm minimization. However, the high computational complexity involved in the ℓ1tracker may hamper its applications in real-time processing scenarios. Here we propose Real-time Com-pressive Sensing Tracking (RTCST) by exploiting the signal recovery power of Compressive Sensing (CS). Dimensionality reduction and a customized Orthogonal Matching Pursuit (OMP) algorithm are adopted to accelerate the CS tracking. As a result, our algorithm achieves a realtime speed that is up to 5,000 times faster than that of the ℓ1tracker. Meanwhile, RTCST still produces competitive (sometimes even superior) tracking accuracy compared to the ℓ1tracker. Furthermore, for a stationary camera, a refined tracker is designed by integrating a CS-based background model (CSBM) into tracking. This CSBM-equipped tracker, termed RTCST-B, outperforms most state-of-the-art trackers in terms of both accuracy and robustness. Finally, our experimental results on various video sequences, which are verified by a new metric - Tracking Success Probability (TSP), demonstrate the excellence of the proposed algorithms.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Donoho, D. L. (2004) · Cited by 16,703
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Tropp, J. A., & Gilbert, A. C. (2007) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.909108 · Cited by 9,653
Robust Face Recognition via Sparse Representation
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.79 · Cited by 9,388
Matching pursuits with time-frequency dictionaries
Mallat, S. G., & Zhang, Z. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258082 · Cited by 9,112
Stable signal recovery from incomplete and inaccurate measurements
Candès, E. J., Romberg, J. K., & Tao, T. (2006) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20124 · Cited by 7,036
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
On sequential Monte Carlo sampling methods for Bayesian filtering
Doucet, A., Godsill, S., & Andrieu, C. (2000) · Statistics and Computing · doi:10.1023/a:1008935410038 · Cited by 4,411
Cited by
Real-Time Compressive Tracking
Zhang, K., Zhang, L., & Yang, M.-H. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33712-3_62 · Cited by 1,427
Facial Landmark Detection by Deep Multi-task Learning
Zhang, Z., Luo, P., Loy, C. C., & Tang, X. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10599-4_7 · Cited by 1,365
Real time robust L1 tracker using accelerated proximal gradient approach
Bao, C., Wu, Y., Ling, H., & Ji, H. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247881 · Cited by 835
Zhang, K., Zhang, L., & Yang, M.-H. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2315808 · Cited by 536
Learning Deep Representation for Face Alignment with Auxiliary Attributes
Zhang, Z., Luo, P., Loy, C. C., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2469286 · Cited by 424
Metadata
- Authors
- Li, H., Shen, C., & Shi, Q.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 1305-1312
- OpenAlex
- W2002406878
- Cited by
- 297
- References
- 20
- FWCI
- 23.44
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Real-time visual tracking using compressive sensing. https://app.cochat.ai/papers/10.1109/cvpr.2011.5995483
- The paper
- Li, H., Shen, C., & Shi, Q. (2011). Real-time visual tracking using compressive sensing. CVPR 2011. https://doi.org/10.1109/cvpr.2011.5995483
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
