# Strip-Fusion: Spatiotemporal Fusion for Multispectral Pedestrian Detection

arXiv: https://arxiv.org/abs/2601.18008 · HTML: https://mlsanity.com/paper/2601.18008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 25 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation](https://mlsanity.com/paper/2005.03572/) (2020)
- [Cross-Modality Fusion Transformer for Multispectral Object Detection](https://mlsanity.com/paper/2111.00273/) (2021)
- [Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems](https://mlsanity.com/paper/2008.03043/) (2020)
- [Fusion-Mamba for Cross-modality Object Detection](https://mlsanity.com/paper/2404.09146/) (2024)
- [Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation](https://mlsanity.com/paper/1808.04818/) (2018)
- [TAda! Temporally-Adaptive Convolutions for Video Understanding](https://mlsanity.com/paper/2110.06178/) (2021)
- [MS-DETR: Multispectral Pedestrian Detection Transformer with Loosely Coupled Fusion and Modality-Balanced Optimization](https://mlsanity.com/paper/2302.00290/) (2023)
- [Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection](https://mlsanity.com/paper/2403.01300/) (2024)
- [Strip-MLP: Efficient Token Interaction for Vision MLP](https://mlsanity.com/paper/2307.11458/) (2023)
- [CFMW: Cross-modality Fusion Mamba for Robust Object Detection under Adverse Weather](https://mlsanity.com/paper/2404.16302/) (2024)
- [Multi-Modal Pedestrian Detection with Large Misalignment Based on Modal-Wise Regression and Multi-Modal IoU](https://mlsanity.com/paper/2107.11196/) (2021)
- [MambaST: A Plug-and-Play Cross-Spectral Spatial-Temporal Fuser for Efficient Pedestrian Detection](https://mlsanity.com/paper/2408.01037/) (2024)

## Abstract (arXiv)

Pedestrian detection is a critical task in robot perception. Multispectral modalities (visible light and thermal) can boost pedestrian detection performance by providing complementary visual information. Several gaps remain with multispectral pedestrian detection methods. First, existing approaches primarily focus on spatial fusion and often neglect temporal information. Second, RGB and thermal image pairs in multispectral benchmarks may not always be perfectly aligned. Pedestrians are also challenging to detect due to varying lighting conditions, occlusion, etc. This work proposes Strip-Fusion, a spatial-temporal fusion network that is robust to misalignment in input images, as well as varying lighting conditions and heavy occlusions. The Strip-Fusion pipeline integrates temporally adaptive convolutions to dynamically weigh spatial-temporal features, enabling our model to better capture pedestrian motion and context over time. A novel Kullback-Leibler divergence loss was designed to mitigate modality imbalance between visible and thermal inputs, guiding feature alignment toward the more informative modality during training. Furthermore, a novel post-processing algorithm was developed to reduce false positives. Extensive experimental results show that our method performs competitively for both the KAIST and the CVC-14 benchmarks. We also observed significant improvements compared to previous state-of-the-art on challenging conditions such as heavy occlusion and misalignment.