Improving Object Detection in Videos: A Comprehensive Evaluation of Faster R-CNN Employed in Partial Occlusion Handling-Reference-Cited by-同舟云学术

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Improving Object Detection in Videos: A Comprehensive Evaluation of Faster R-CNN Employed in Partial Occlusion Handling

Published:2024-03-01 Issue: Volume: Page:207-212
ISSN:
Container-title:2024 20th IEEE International Colloquium on Signal Processing & Its Applications (CSPA)
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Author:

Yap Jun Jie¹,Yong Yoke Leng¹,Jasser Muhammed Basheer¹,Ajibade Samuel-Soma M.¹,Al-Hadi Ismail Ahmed Al-Qasem²

Affiliation:

1. Sunway University,Department of Computing and Information Systems,Sunway City,Selangor,Malaysia

2. Institute of Computer Science & Digital Innovation, UCSI University,Kuala Lumpur,Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10525259/10525269/10525276.pdf?arnumber=10525276

Reference17 articles.

1. Visibility, occlusion, and the aspect graph

2. Occlusion Detection and Handling: A Review

3. Amodal Instance Segmentation

4. Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling

5. Amodal Panoptic Segmentation

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