An Efficient Framework for Object Tracking in Video Surveillance
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The visual tracking task has attracted interest because of its relevance in applications including video surveillance and autonomous driving.
“The task has attracted a lot of interest because of its enormous relevance in many real-world applications, including video surveillance [82], autonomous driving [83], etc.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [82]
Cites
Adaptive Color Attributes for Real-Time Visual Tracking
Danelljan, M., Khan, F. S., Felsberg, M., & van de Weijer, J. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.143 · Cited by 1,541 · see citing works
Spatially supervised recurrent convolutional neural networks for visual object tracking
Ning, G., Zhang, Z., Huang, C., Ren, X., Wang, H., Cai, C., & He, Z. (2017) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2017.8050867 · Cited by 276 · see citing works
Multi-task deep visual-semantic embedding for video thumbnail selection
Liu, W., Mei, T., Zhang, Y., Che, C., & Luo, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298994 · Cited by 260 · see citing works
Object Tracking with Multi-View Support Vector Machines
Zhang, S., Yu, X., Sui, Y., Zhao, S., & Zhang, L. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2390044 · Cited by 115 · see citing works
Crowd event recognition using HOG tracker
Garate, C., Bilinsky, P., & Brémond, F. (2009) · IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS) · doi:10.1109/pets-winter.2009.5399727 · Cited by 53 · see citing works
Robust Vision-based Multiple Moving Object Detection and Tracking from Video Sequences
Khalifa, O. O., Malek, N. F. A., & Ahmed, K. I. (2018) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v10.i2.pp817-826 · Cited by 4 · see citing works
A novel foreground region analysis using NCP-DBP texture pattern for robust visual tracking
Mohanapriya, D., & Mahesh, K. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4409-3 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works
- Authors
- Mohanapriya, D., & Mahesh, K.
- Affiliations
- Alagappa University (IN)
- Venue
- The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems
- Publisher
- Elsevier
- Published
- 2020
- Pages
- 65-74
- OpenAlex
- W3011623526
- Cited by
- 7
- References
- 7
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 1 · 2021: 4
- FWCI
- 2.87
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Elsevier eBooks (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 Vision and Imaging
- Keywords
- object tracking, video surveillance, shadow removal, visual object tracking, tracking accuracy
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
