Remote Sensing Image Target Recognition System of Tennis Sports based on CAMSHIFT Algorithm

Jiang, Zerong; Li, Ruoguo; Chen, Zhu

doi:10.1109/iciscet56785.2022.00042

2022 International Conference on Information System, Computing and Educational Technology (ICISCET) 2022

DOI: 10.1109/iciscet56785.2022.00042

|View full text |Cite

Remote Sensing Image Target Recognition System of Tennis Sports based on CAMSHIFT Algorithm

Zerong Jiang

Ruoguo Li

Zhu Chen

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Robust, Resilient Enhanced CAMSHIFT Model: Advancing Face Detection and Tracking Stability in Challenging Environments

Alex Kok Bin See,

Keith Han Wee Goh

2024

Malaysian J. Sci. Adv. Tech.

View full text Add to dashboard Cite

This paper presents the development of a robust CAMSHIFT model for theoretical face detection and tracking. The proposed model integrates innovative techniques such as Perceptual Grouping, three Connected Component Operators, Weighted Adaptive Colour Histogram, and Selective Adaptation. Experimental results highlight its superior performance across scenarios like occlusions, varying illumination, near/far face tracking, skin-like background tracking, and disturbance from multiple faces. The normalized log-likelihood index serves as a robust indicator for face tracking analysis. Connected Component operations provide strong markers for error detection in video sequences. The enhanced CAMSHIFT algorithm exhibits resilience and stability, even in the presence of occlusions. Comparisons with the original CAMSHIFT reveal the enhanced model's superiority, extending tracking range to 500 cm, a calculated enhancement of 42.9 percent improvement. The study consistently favours the robust and resilient CAMSHIFT model in tracking against skin-like backgrounds and disturbances. Despite webcam convenience in used for algorithm development, the benefits of high-performance camera systems are envisioned for future research. This model is a significant advancement in face detection methods, promising improved adaptability and tracking capabilities.

show abstract

Robust, Resilient Enhanced CAMSHIFT Model: Advancing Face Detection and Tracking Stability in Challenging Environments

Alex Kok Bin See,

Keith Han Wee Goh

2024

Malaysian J. Sci. Adv. Tech.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Remote Sensing Image Target Recognition System of Tennis Sports based on CAMSHIFT Algorithm

Cited by 1 publication

References 9 publications

Robust, Resilient Enhanced CAMSHIFT Model: Advancing Face Detection and Tracking Stability in Challenging Environments

Robust, Resilient Enhanced CAMSHIFT Model: Advancing Face Detection and Tracking Stability in Challenging Environments

Contact Info

Product

Resources

About