MTF-Net: Multi-Modal Temporal Feature Fusion Network for Pedestrian Intention Prediction
Accurately predicting pedestrian intentions is crucial for ensuring safe and proactive interaction between autonomous vehicles and pedestrians. However, existing approaches often depend on architectures that either model temporal dependencies within individual modalities or fuse modalities only at coarse semantic levels. To address these limitations, we propose MTF-Net, a novel Multi-Modal Temporal Feature Fusion Network that jointly models kinematic, appearance, and contextual cues for pedestrian intention prediction. MTF-Net integrates four complementary modalities-bounding-box dynamics, human pose keypoints, local context, and scene-level semantics within a recurrent fusion framework e...
| Authors | Md Mahfuzur Rahman, Pengzhan Zhou, A. F. M. Abdun Noor et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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