The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Object Detection, Classification and Tracking Methods for Video Surveillance: A Review
Abstract
Video surveillance is going through an enormous change in the last decade and lots of research is undergoing in this area. As object tracking is an integral part of such systems, it becomes necessary to review all state-of-the-art methods and approaches which belongs to object detection, classification, and tracking. This paper consists of all such methods in a classified manner. After detecting object, classification is done in order to track that later. Object tracking is the method to locate the target in subsequent frames of video. There are also some challenges in video surveillance which make it complex. Object tracking is characterized on some parameters like Illumination variation, co-ordinates matching, environmental issues, tracking object’s variety, pose variation, occlusion, motion blur etc. A vast classification is considered for review and complete analysis of all possible techniques so that each aspect is thus covered along with present research undergoing in that area.
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“The frame differencing and optical flow algorithms both compare consecutive frames to detect changes in pixel values, but optical flow algorithms are more suitable for dynamically changing environments, which can process complex motion patterns [69][70][71].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [71]
Citations
Cites
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S. R., Gordon, N., & Clapp, T. C. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,531
A comparison of methods for multiclass support vector machines
Hsu, C., & Lin, C. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,923
Unscented Filtering and Nonlinear Estimation
Julier, S., & Uhlmann, J. K. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2003.823141 · Cited by 6,798
Face Description with Local Binary Patterns: Application to Face Recognition
Ahonen, T., Hadid, A., & Pietikäinen, M. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.244 · Cited by 5,592
Yılmaz, A., Javed, O., & Shah, M. A. (2006) · ACM Computing Surveys · doi:10.1145/1177352.1177355 · Cited by 4,902
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
Kalman Filtering With Intermittent Observations
Sinopoli, B., Luca Schenato, B., Franceschetti, M., Poolla, K. R., Jordan, M. I., & Sastry, S. (2004) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.834121 · Cited by 2,570
Background subtraction techniques: a review
Piccardi, M. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2004.1400815 · Cited by 2,106
Multiple hypothesis tracking for multiple target tracking
Blackman, S. S. (2004) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2004.1263228 · Cited by 1,415
Avidan, S. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.53 · Cited by 1,050
Research works printed but not linked: 1
- mohanty, 2015, Article: A Survey on Moving Object Detection using Background Subtraction Methods in Video, IJCA Proceedings on National Conference on Knowledge Innovation in Technology and Engineering (NCKITE 2015), 5
Cited by
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A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134
Metadata
- Authors
- Joshi, R. C., Joshi, M., Singh, A. G., & Mathur, S.
- Affiliations
- Govind Ballabh Pant University of Agriculture and Technology (IN)
- Venue
- 2018 4th International Conference on Computing Communication and Automation (ICCCA)
- Publisher
- IEEE
- Published
- 2018-12
- Pages
- 1-7
- OpenAlex
- W2965241815
- Cited by
- 17
- References
- 57
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 4 · 2022: 2 · 2021: 3 · 2020: 3
- FWCI
- 0.45
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computing, Communication and Automation (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- video surveillance, object detection, object classification, object tracking, illumination variation, occlusion, pose variation, motion blur, coordinate matching
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- CoChat. (2026). Citation check: Object Detection, Classification and Tracking Methods for Video Surveillance: A Review. https://app.cochat.ai/papers/10.1109/ccaa.2018.8777708
- The paper
- Joshi, R. C., Joshi, M., Singh, A. G., & Mathur, S. (2018). Object Detection, Classification and Tracking Methods for Video Surveillance: A Review. 2018 4th International Conference on Computing Communication and Automation (ICCCA). https://doi.org/10.1109/ccaa.2018.8777708
