Published June 12, 2024
| Version v1
Conference paper
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A Comprehensive Study of Object Tracking Methods
Creators
- π¨ππ πππππππ πππ π²πππππ π»ππππππππππa (Data collector)1
- π¨ππ πππππππππ ππππππππ π²ππ πππππππ ππππ (Data collector)2
- πͺπππππππππππππ πΊππππππ π¨ππππππππ (Data collector)3
- 1. Assosciate professor at Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Department of Computer Systems
- 2. Graduate student at Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Department of Computer Systems
- 3. Undergraduate student at Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Department of Computer Systems
Description
Object tracking is a key problem in many computer vision
applications, including surveillance, automobile navigation, autonomous robot
navigation, and so on. It detects fascinating moving items and tracks them from
frame to frame. Its primary function is to detect and track a moving object or
numerous objects in image sequences. Normally, there are three stages to video
analysis: object detection, object tracking, and object reorganization. This paper
provides a brief overview of several video object tracking approaches, including
point tracking, kernel tracking, and Silhouette tracking algorithms
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Abdurashidova Kamola 105-108.pdf
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Additional details
References
- [1] Yilmaz, O. Javed and M. Shah, 'Object tracking: A survey', ACM Computing Surveys, 2006
- [2] Comaniciu, Dorin, Visvanathan Ramesh, and Peter Meer. 'Kernel-based object tracking'. Pattern Analysis and Machine Intelligence, IEEE Transactions on 25.5, pp. 564-577, 2003
- [3] Comaniciu, Dorin, and Peter Meer. 'Mean shift: A robust approach toward feature space analysis' Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.5, pp. 603-619, 2002
- [4] Veenman, Cor J., Marcel JT Reinders, and Eric Backer. 'Resolving motion correspondence for densely moving points'. Pattern Analysis and Machine Intelligence, IEEE Transactions on 23.1, pp. 54-72, 2001
- [5] Shafique, K. AND Shah, M. 'A non-iterative greedy algorithm for multi-frame point correspondence' In IEEE International Conference on Computer Vision (ICCV) 2003