# MTF-Net: Multi-Modal Temporal Feature Fusion Network for Pedestrian Intention Prediction

arXiv: https://arxiv.org/abs/2609.20178 · HTML: https://mlsanity.com/paper/2609.20178/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 58 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 22 of 22)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion](https://mlsanity.com/paper/2304.01893/) (2023)
- [SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2104.01528/) (2021)
- [Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction](https://mlsanity.com/paper/2002.08945/) (2020)
- [YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss](https://mlsanity.com/paper/2204.06806/) (2022)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs](https://mlsanity.com/paper/2005.06582/) (2020)
- [Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention](https://mlsanity.com/paper/2104.05485/) (2021)
- [Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2206.05712/) (2022)
- [PACER+: On-Demand Pedestrian Animation Controller in Driving Scenarios](https://mlsanity.com/paper/2404.19722/) (2024)
- [Multi-Modal Hybrid Architecture for Pedestrian Action Prediction](https://mlsanity.com/paper/2012.00514/) (2020)
- [RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features](https://mlsanity.com/paper/2008.11647/) (2020)
- [Is attention to bounding boxes all you need for pedestrian action prediction?](https://mlsanity.com/paper/2107.08031/) (2021)
- [VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision](https://mlsanity.com/paper/2304.03135/) (2023)
- [IDD-X: A Multi-View Dataset for Ego-relative Important Object Localization and Explanation in Dense and Unstructured Traffic](https://mlsanity.com/paper/2404.08561/) (2024)
- [Causal reasoning in typical computer vision tasks](https://mlsanity.com/paper/2307.13992/) (2023)
- [PIP-Net: Pedestrian Intention Prediction in the Wild](https://mlsanity.com/paper/2402.12810/) (2024)
- [ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory Forecasting](https://mlsanity.com/paper/2506.09626/) (2025)

## Abstract (arXiv)

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 enhanced by gated linear units (GLUs). These GLU-based modules adaptively regulate cross-modal information flow, enabling interpretable and efficient feature interaction across temporal scales. Through three dedicated temporal encoding branches and an attention-guided fusion head, the proposed model robustly anticipates pedestrian crossing intentions several frames before they occur. Extensive evaluations on the PIE and JAAD benchmarks demonstrate that MTF-Net surpasses recent transformer- and graph-based models, achieving up to 0.95 AUC on PIE and 0.94 AUC on JAAD, while maintaining real-time performance. The results highlight that reliable pedestrian intention prediction arises from principled multi-modal fusion rather than excessive architectural complexity.