Enhanced Real-Time Object Tracking and Segmentation Using YOLOv8n with ASCNN, HOF-SG, and SEAT

Authors

  • Guru Prasad M Bhat,Nagaraj G. Cholli

Keywords:

Dynamic Environments; Neural Networks; Adaptive Scale CNN; Optical Flow; Autonomous Navigation

Abstract

Real-time object tracking and its segmentation from the surrounding environment is a complex process when thereare strong occlusions and fast motion. This paper proposes a strong framework that fused YOLOv8n withAdaptive Scale Convolutional Neural Network (ASCNN), Hybrid Optical Flow with Semantic Guidance (HOF SG), and Semantic-Enhanced Adaptive

References

S. K. Pal, A. Pramanik, J. Maiti, and P. Mitra, “Deep learning in multi-object detection and tracking: state of the art,” Appl. Intell., vol. 51, no. 9, pp. 6400–6429, Sep. 2021, doi: 10.1007/S10489-021-022937/FIGURES/4.

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Published

2024-07-20

How to Cite

Guru Prasad M Bhat,Nagaraj G. Cholli. (2024). Enhanced Real-Time Object Tracking and Segmentation Using YOLOv8n with ASCNN, HOF-SG, and SEAT. Journal of Computational Analysis and Applications (JoCAAA), 33(07), 2926–2945. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3909

Issue

Vol. 33 No. 07 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.