CASIA OpenIR  > 模式识别国家重点实验室
Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection
Lu Zhang1,3; Xiangyu Zhu2,3; Xiangyu Chen4; Xu Yang1,3; Zhen Lei2,3; Zhiyong Liu1,3,4
2019
Conference NameIEEE International Conference on Computer Vision
Conference Date2019.10.27-2019.11.02
Conference PlaceSeoul, Korea
Abstract

Multispectral pedestrian detection has shown great advantages under poor illumination conditions, since the thermal modality provides complementary information for the color image. However, real multispectral data suffers from the position shift problem, i.e. the color-thermal image pairs are not strictly aligned, making one object has different positions in different modalities. In deep learning based methods, this problem makes it difficult to fuse the feature maps from both modalities and puzzles the CNN training. In this paper, we propose a novel Aligned Region CNN (AR-CNN) to handle the weakly aligned multispectral data in an end-to-end way. Firstly, we design a Region Feature Alignment (RFA) module to capture the position shift and adaptively align the region features of the two modalities. Secondly, we present a new multimodal fusion method, which performs feature re-weighting to select more reliable features and suppress the useless ones. Besides, we propose a novel RoI jitter strategy to improve the robustness to unexpected shift patterns of different devices and system settings. Finally, since our method depends on a new kind of labelling: bounding boxes that match each modality, we manually relabel the KAIST dataset by locating bounding boxes in both modalities and building their relationships, providing a new KAIST-Paired Annotation. Extensive experimental validations on existing datasets are performed, demonstrating the effectiveness and robustness of the proposed method. Code and data are available at: https://github.com/luzhang16/AR-CNN.

Indexed BySCI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25840
Collection模式识别国家重点实验室
复杂系统管理与控制国家重点实验室
Affiliation1.SKL-MCCS, Institute of Automation, Chinese Academy of Sciences
2.CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences
3.University of Chinese Academy of Sciences
4.CEBSIT, Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Lu Zhang,Xiangyu Zhu,Xiangyu Chen,et al. Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection[C],2019.
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