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Visual detection and tracking algorithms for human motion.
- Source :
- Multimedia Tools & Applications; Dec2023, Vol. 82 Issue 30, p47165-47188, 24p
- Publication Year :
- 2023
-
Abstract
- In dense scenes, a large number of individuals can introduce serious complications for motion detection, such as blurred vision, chaotic scenes, and complex behaviours. For low-density pedestrian detection and tracking algorithms, the accuracy is greatly reduced for both detection and tracking. High-density detection or tracking fails too when these problems are encountered in high-density scenes. In light of the above problems, a detection algorithm and a tracking algorithm based on the human head and shoulder model are proposed. A support vector machine is used to train the classifier by machine learning. The detection algorithm proposed in this paper achieves a detection accuracy of 94% by using the MIT and INRIA datasets. The average accuracy of pedestrian tracking in high-density scenes is approximately 95%. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13807501
- Volume :
- 82
- Issue :
- 30
- Database :
- Complementary Index
- Journal :
- Multimedia Tools & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 173927106
- Full Text :
- https://doi.org/10.1007/s11042-023-15231-1