Abstract
Person re-identification (ReID) plays a critical role in applications like security surveillance and criminal investigations by matching individuals across large image galleries captured by non-overlapping cameras. Traditional ReID methods rely on unimodal inputs, typically images, but face limitations due to challenges like occlusions, lighting changes, and pose variations. While advancements in image-based and text-based ReID systems have been made, the integration of both modalities has remained under-explored. This paper presents FusionSegReID, a multimodal model that combines both image and text inputs for enhanced ReID performance. By leveraging the complementary strengths of these modalities, our model improves matching accuracy and robustness, particularly in complex, real-world scenarios where one modality may struggle. Our experiments show significant improvements in Top-1 accuracy and mean Average Precision (mAP) for ReID, as well as better segmentation results in challenging scenarios like occlusion and low-quality images. Ablation studies further confirm that multimodal fusion and segmentation modules contribute to enhanced re-identification and mask accuracy. The results show that FusionSegReID outperforms traditional unimodal models, offering a more robust and flexible solution for real-world person ReID tasks.
Abstract (translated)
人员重新识别（ReID）在安全监控和刑事调查等应用中扮演着关键角色，通过将个体跨多个非重叠摄像头捕获的大规模图像库进行匹配。传统的ReID方法依赖于单一模态输入，通常是图像，但由于遮挡、光照变化和姿态变异等问题的挑战性，这些方法面临限制。尽管基于图像和文本的ReID系统的改进已取得进展，但两种模式结合的研究却相对较少。本文介绍了FusionSegReID模型，这是一个多模态模型，它结合了图像和文本输入来增强ReID性能。通过利用这两种模式互补的优势，我们的模型提高了匹配准确性和鲁棒性，在单一模式可能遇到困难的复杂现实场景中尤为突出。实验结果显示，与传统单模态模型相比，FusionSegReID在Top-1精度和平均精度（mAP）方面显著提升了人员重新识别性能，并且在遮挡和低质量图像等具有挑战性的环境中实现了更优的分割结果。消融研究表明，多模态融合和分割模块对于增强重识别准确性和掩码准确性起到了重要作用。总之，实验结果表明FusionSegReID超越了传统的单模态模型，在实际应用中提供了更为稳健且灵活的解决方案以应对人员重新识别任务。
URL
https://arxiv.org/abs/2503.21595
