When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset

• URL: http://arxiv.org/abs/2407.10125v1
• Date: Sun, 14 Jul 2024 09:16:49 GMT
• Title: When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset
• Authors: Yi Zhang, Wang Zeng, Sheng Jin, Chen Qian, Ping Luo, Wentao Liu,
• Abstract summary: This paper introduces MMPedestron, a novel generalist model for multimodal perception. The proposed approach comprises a unified encoder for modal representation and fusion and a general head for pedestrian detection. With multi-modal joint training, our model achieves state-of-the-art performance on a wide range of pedestrian detection benchmarks.
• Score: 40.24765100535353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent years have witnessed increasing research attention towards pedestrian detection by taking the advantages of different sensor modalities (e.g. RGB, IR, Depth, LiDAR and Event). However, designing a unified generalist model that can effectively process diverse sensor modalities remains a challenge. This paper introduces MMPedestron, a novel generalist model for multimodal perception. Unlike previous specialist models that only process one or a pair of specific modality inputs, MMPedestron is able to process multiple modal inputs and their dynamic combinations. The proposed approach comprises a unified encoder for modal representation and fusion and a general head for pedestrian detection. We introduce two extra learnable tokens, i.e. MAA and MAF, for adaptive multi-modal feature fusion. In addition, we construct the MMPD dataset, the first large-scale benchmark for multi-modal pedestrian detection. This benchmark incorporates existing public datasets and a newly collected dataset called EventPed, covering a wide range of sensor modalities including RGB, IR, Depth, LiDAR, and Event data. With multi-modal joint training, our model achieves state-of-the-art performance on a wide range of pedestrian detection benchmarks, surpassing leading models tailored for specific sensor modality. For example, it achieves 71.1 AP on COCO-Persons and 72.6 AP on LLVIP. Notably, our model achieves comparable performance to the InternImage-H model on CrowdHuman with 30x smaller parameters. Codes and data are available at https://github.com/BubblyYi/MMPedestron.

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