Dynamic Saliency Modeling for Enhanced Video Analysis: A Robust Spatio-Temporal Approach (RSTA)

Registration ID: IJNRD_216541

Published ID: IJNRD2404345

DOI: Click Here to Get

Vidya V

Saliency detection, Video analysis, Contrast based saliency

This research introduces a dynamic saliency modeling system for video analysis, combining long-term inter-batch information and color contrast computation to enhance robustness. Leveraging background and foreground appearance models, RGB history handling, and precise saliency adjustment, the proposed method ensures accuracy in capturing visual patterns over time. Spatio-temporal gradient mapping and Robust Principal Component Analysis contribute to contrast-based saliency mapping, demonstrating superior performance in compression efficiency and precision. Experimental results, evaluated against state-of-the-art algorithms, showcase the system's consistent excellence across diverse video sequences and resolutions. The proposed algorithm Robust Spatio-Temporal Approach (RSTA) stands as a promising contribution to computer vision and video analysis applications, affirming its potential impact on future research in the field.

Published Paper   E-Certificate

Vidya V (April-2024). Dynamic Saliency Modeling for Enhanced Video Analysis: A Robust Spatio-Temporal Approach (RSTA). INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), d382-d390. https://ijnrd.org/papers/IJNRD2404345.pdf

Volume 9 Issue 4, April-2024

Pages : d382-d390

Paper Reg. ID: IJNRD_216541

Published Paper Id: IJNRD2404345

Downloads: 000121980

Research Area: Computer Science & Technology 

Country: Bangalore, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404345.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404345

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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