Summary of Robust Ensemble Person Re-identification Via Orthogonal Fusion with Occlusion Handling, by Syeda Nyma Ferdous et al.
Robust Ensemble Person Re-Identification via Orthogonal Fusion with Occlusion Handling
by Syeda Nyma Ferdous, Xin Li
First submitted to arxiv on: 29 Mar 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 addresses the challenge of occlusion in person reidentification (ReID), which is crucial for robustly identifying individuals across varying poses and appearances. The authors propose a deep ensemble model that combines convolutional neural networks (CNNs) and Transformers to generate robust feature representations, particularly on low-resolution edge cameras. To tackle occluded regions without manual labeling, they develop an ensemble learning approach inspired by the analogy between arbitrarily shaped occluded regions and robust feature representation. The proposed model, named Orthogonal Fusion with Occlusion Handling (OFOH), utilizes masked autoencoders (MAEs) and global-local feature fusion for robust person identification. Furthermore, the authors introduce a part occlusion-aware Transformer that learns a feature space robust to occluded regions. Experimental results on several ReID datasets demonstrate the effectiveness of OFOH, outperforming competing methods in terms of rank-1 and mean Average Precision (mAP) performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better recognize people in different situations by developing a new way to improve person reidentification. The team created a special model that combines two types of artificial intelligence: convolutional neural networks (CNNs) and Transformers. This combination makes the model more robust, which means it can handle situations where parts of a person are hidden or partially visible. The authors also came up with an innovative way to deal with occluded regions without needing people to label them manually. They tested their approach on several datasets and found that it outperformed other methods in recognizing people. |
Keywords
» Artificial intelligence » Ensemble model » Mean average precision » Transformer
