Research map / Video / Video segmentation and tracking
Recurrent Convolutional Network for Video-Based Person Re-Identification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add recurrent connections to the CNN feature sequence (ablation) | Video re-identification network · iLIDS-VID and PRID-2011 | CMC | Helped |
| Add optical-flow channels alongside colour input (ablation) | Video re-identification network · iLIDS-VID and PRID-2011 | CMC | Helped |
| Replace max-pooling with mean-pooling over time (ablation) | CNN sequence representation · iLIDS-VID | CMC | Helped |
| Use mean-pooling over the sequence rather than comparing individual frames (ablation) | CNN sequence representation · iLIDS-VID | CMC | Helped |
| Increase probe or gallery sequence length (ablation) | Video re-identification system · iLIDS-VID | Rank 1 CMC | Helped |
| Use full video sequences instead of single images (ablation) | Ours versus Ours* · PRID-2011 cross-dataset | Rank 1 CMC (%): 14 -> 28 | Helped |
Closest papers by meaning
- Temporal-Enhanced Convolutional Network for Person Re-Identification (AAAI 2018 · similarity 0.92)
- See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-Identification (CVPR 2017 · similarity 0.92)
- Multi-Rate Gated Recurrent Convolutional Networks for Video-Based Pedestrian Re-Identification (AAAI 2018 · similarity 0.91)
- Spatial-Temporal Graph Convolutional Network for Video-Based Person Re-Identification (CVPR 2020 · similarity 0.90)
- Keypoint Message Passing for Video-Based Person Re-identification (AAAI 2022 · similarity 0.89)
- Person Re-Identification With Cascaded Pairwise Convolutions (CVPR 2018 · similarity 0.88)
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