Robust Ensemble Person Re-Identification via Orthogonal Fusion with Occlusion Handling
This work addresses occlusion challenges in person re-identification, which is important for surveillance and security applications, but it appears incremental as it builds on existing ensemble and occlusion-handling methods.
The paper tackled the problem of occlusion in person re-identification by proposing an ensemble model combining CNN and Transformer architectures, achieving competitive rank-1 and mAP performance on several datasets.
Occlusion remains one of the major challenges in person reidentification (ReID) as a result of the diversity of poses and the variation of appearances. Developing novel architectures to improve the robustness of occlusion-aware person Re-ID requires new insights, especially on low-resolution edge cameras. We propose a deep ensemble model that harnesses both CNN and Transformer architectures to generate robust feature representations. To achieve robust Re-ID without the need to manually label occluded regions, we propose to take an ensemble learning-based approach derived from the analogy between arbitrarily shaped occluded regions and robust feature representation. Using the orthogonality principle, our developed deep CNN model makes use of masked autoencoder (MAE) and global-local feature fusion for robust person identification. Furthermore, we present a part occlusion-aware transformer capable of learning feature space that is robust to occluded regions. Experimental results are reported on several Re-ID datasets to show the effectiveness of our developed ensemble model named orthogonal fusion with occlusion handling (OFOH). Compared to competing methods, the proposed OFOH approach has achieved competent rank-1 and mAP performance.