The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
A survey on moving object tracking using image processing
Abstract
Image processing is a method of extracting some useful information by converting image into digital inform by performing some operations on it. Object detection and tracking are the task that is important and challenging such as video surveillance and vehicle navigation. Video surveillance is a technology which works in dynamic environment in various events such as sports, public safety, and management of traffic. This paper reviews the various challenges and aspects of detection and tracking of objects.
Overview
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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
“For example, point-based, kernel-based, and silhouette-based approaches can be applied to object tracking.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [89]
Citations
Cites
Yılmaz, A., Javed, O., & Shah, M. A. (2006) · ACM Computing Surveys · doi:10.1145/1177352.1177355 · Cited by 4,909
Geodesic active contours and level sets for the detection and tracking of moving objects
Paragios, N., & Deriche, R. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.841758 · Cited by 1,006
Analysis of Particle Swarm Optimization Algorithm
Qing-hai, B. (2010) · Computer and Information Science · doi:10.5539/cis.v3n1p180 · Cited by 746
Robust techniques for background subtraction in urban traffic video
Cheung, S.-C. S., & Kamath, C. (2004) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.526886 · Cited by 682
A Survey on Moving Object Detection and Tracking in Video Surveillance System
Joshi, K. A., & Thakore, D. G. (2012) · Cited by 174
Moving Object Tracking Using Kalman Filter
Gunjal, P. R., Gunjal, B. R., Shinde, H. A., et al. (2018) · 2018 International Conference On Advances in Communication and Computing Technology (ICACCT) · doi:10.1109/icacct.2018.8529402 · Cited by 162
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
Object Tracking and Detecting Based on Adaptive Background Subtraction
Zhang, R., & Ding, J. (2012) · Procedia Engineering · doi:10.1016/j.proeng.2012.01.139 · Cited by 76
(2013) · Cited by 76
Research works printed but not linked: 6
- chauhan, 2013, Moving Object Tracking Using Gaussian Mixture Model And Optical Flow, International Journal of Advanced Research in Computer Science and Software Engineering
- athanesious, 2012, Systematic Survey on Object Tracking Methods in Video, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 242
- banerjee, 2008, Multi person Tracking Using Kalman Filter
- athanesious j, 2013, Implementation and Comparison of Kernel and Silhouette Based Object Tracking, International Journal of Advanced Research in Computer Engineering & Technology, 1298
- mishra, 2012, Multiple Object Tracking by Kernel Based Centroid Method for Improve Localization, International Journal of Advanced Research in Computer Science and Software Engineering, 137
- athanesious, 2012, Systematic Survey on Object Tracking Methods in Video, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 242
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
“The selection of an appropriate method for a given task depends on the specific goals and requirements of the task [89].”
Method
Metadata
- Authors
- Balaji, S. R., & Karthikeyan, S.
- Affiliations
- Sathyabama Institute of Science and Technology (IN)
- Venue
- 2017 11th International Conference on Intelligent Systems and Control (ISCO)
- Publisher
- IEEE
- Published
- 2017-01
- Pages
- 469-474
- OpenAlex
- W2588179149
- Cited by
- 86
- References
- 16
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 8 · 2023: 15 · 2022: 9 · 2021: 12 · 2020: 14 · 2019: 16 · 2018: 6
- FWCI
- 2.88
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Intelligent Systems and Control (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
- moving object tracking, object detection, video surveillance, image processing, vehicle navigation, traffic management, public safety
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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.
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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.
