Research map / Detection and segmentation / Tracking
Learning a Proposal Classifier for Multiple Object Tracking
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposal-based LPC_MOT framework instead of MPNTrack | MOT tracking · MOT17 | IDF1: 61.7 -> 66.8 | Helped |
| Use the proposal-based LPC_MOT framework instead of MPNTrack | MOT tracking · MOT20 | IDF1: 59.1 -> 62.5 | Helped |
| Use the proposal-based LPC_MOT framework instead of Lif_T | MOT tracking · MOT16 | IDF1: 64.7 -> 67.6 | Helped |
| Use iterative graph clustering rather than MHT-based proposal generation (ablation) | MOT17 train proposal generation · MOT17 train | Hz: 2.4 -> 21.6 | Helped |
| Increase the proposal-generation iteration count from 1 toward 10 (ablation) | LPC_MOT · MOT17 train | MOTA | Helped |
| Combine spatial-temporal and appearance features rather than use spatial-temporal features only (ablation) | GCN purity classification · MOT17 train | IDF1: 69.4 -> 71.8 | Helped |
| Combine spatial-temporal and appearance features rather than use appearance features only (ablation) | GCN purity classification · MOT17 train | IDF1: 70.3 -> 71.8 | Helped |
| Use BCELoss instead of MSELoss (ablation) | GCN purity classification · MOT17 train | IDF1: 71.2 -> 71.8 | Helped |
| Use GCN rather than TCN for proposal purity classification (ablation) | LPC_MOT · MOT17 train | IDF1: 70.6 -> 71.8 | Helped |
| Use simple de-overlapping rather than iterative greedy inference (ablation) | LPC_MOT · MOT17 train | MOTA: 63.6 -> 63.9 | Helped |
| Use simple de-overlapping rather than iterative greedy inference (ablation) | LPC_MOT · MOT17 train | IDF1: 71.7 -> 71.8 | Helped |
| Use the GCN purity classifier rather than oracle purity labels (ablation) | LPC_MOT · MOT17 train | IDF1: 73.3 -> 71.8 | Hurt |
Closest papers by meaning
- Learning a Neural Solver for Multiple Object Tracking (CVPR 2020 · similarity 0.90)
- Learnable Graph Matching: Incorporating Graph Partitioning With Deep Feature Learning for Multiple Object Tracking (CVPR 2021 · similarity 0.89)
- Multiple Object Tracking as ID Prediction (CVPR 2025 · similarity 0.87)
- Multi-Scene Generalized Trajectory Global Graph Solver with Composite Nodes for Multiple Object Tracking (AAAI 2024 · similarity 0.86)
- OVTR: End-to-End Open-Vocabulary Multiple Object Tracking with Transformer (ICLR 2025 · similarity 0.85)
- DeconfuseTrack: Dealing with Confusion for Multi-Object Tracking (CVPR 2024 · similarity 0.85)
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