Summary of Improving Detection Of Person Class Using Dense Pooling, by Nouman Ahmad
Improving Detection of Person Class Using Dense Pooling
by Nouman Ahmad
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative approach to improve the accuracy of person detection in computer vision. Building upon FasterRCNN, a state-of-the-art model for object detection, the authors develop a novel method that enhances the Region of Interest (ROI) feature extraction process. The key innovation lies in the implementation of dense pooling and 3D modeling, which enables the extraction of relevant features from UV images. This approach is evaluated on the COCO dataset, resulting in significant improvements in person detection accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper aims to improve how well computers can detect people in pictures. It uses a special technique called FasterRCNN that has already shown great results. The researchers then took it one step further by creating an innovative way to extract features from images. This approach was tested on a large dataset of over 6,900 images and showed impressive results in detecting people. |
Keywords
» Artificial intelligence » Feature extraction » Object detection
