The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A review on real time object tracking in video sequences
Abstract
Removed.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“This method is capable of processing occlusion and object fragmentation and merging, as well as various complex object shapes [99].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [99]
Citations
Cites
Adaptive background mixture models for real-time tracking
Stauffer, C., & Grimson, W. E. L. (2003) · 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.1999.784637 · Cited by 6,925
Yılmaz, A., Javed, O., & Shah, M. A. (2006) · ACM Computing Surveys · doi:10.1145/1177352.1177355 · Cited by 4,909
A Survey on Object Detection and Tracking Methods
Singh, S. K., Sharma, U., & Shankaracharya, S. (2015) · IJSRD : international journal for scientific research and development · Cited by 107
A Survey on Object Detection and Tracking Methods
Parekh, H. S., Thakore, D. G., & Jaliya, U. K. (2014) · International Journal of Innovative Research in Computer and Communication Engineering · Cited by 104
Research works printed but not linked: 2
- rahul, 2012, Multiple Object Tracking by Kernel Based Centroid Method for Improve Localization, International Journal of Advanced Research in Computer Science and Software Engineering, 137
- bhajibhakare, 2013, To Detect and Track Moving Object for Surveillance System, International Journal of Innovative Research in Computer and Communication Engineering, 1
Cited by
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134
“This method is capable of processing occlusion and object fragmentation and merging, as well as various complex object shapes [99].”
Method
Metadata
- Authors
- Kothiya, S. V., & Mistree, K. B.
- ORCID
- Mistree, Kinjal B.
- Venue
- 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)
- Publisher
- IEEE
- Published
- 2015-01
- Pages
- 1-4
- OpenAlex
- W1583372434
- Cited by
- 32
- References
- 4
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 5 · 2022: 4 · 2021: 5 · 2020: 6 · 2018: 3 · 2017: 2 · 2016: 2 · 2015: 2
- FWCI
- 1.23
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Electrical, Electronics, Signals, Communication and Optimization (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 › Infrared Target Detection Methodologies
- Keywords
- real-time object tracking, video sequences, video surveillance
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.
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