基于多模态融合的复杂场景人员重识别

Multi-modal Fusion-based Person Re-identification of Individuals in Complex Scenes

人员重识别(re-identification,ReID)是计算机视觉中的关键任务,具有众多实际应用,如视频监控和人员跟踪。通过对各类应用场景的研究,提出了一种新方法,以解决复杂场景下ReID的挑战,包括遮挡、视角变化和光照条件变化等问题。利用多模态融合技术增强了ReID模型的判别能力,使其在具有挑战性的现实场景中更加稳健,并在基准数据集上进行了广泛的试验。结果表明了所提方法的有效性,实现了更为先进的性能。

Person re-identification(ReID)is a critical task in computer vision with numerous practical applications such as video surveillance and personnel tracking.A novel method was proposed to address the challenges of ReID in complex scenarios,including issues like occlusion,changes in viewpoint,and variations in lighting conditions.Multi-modal fusion techniques were utilized to enhance the discriminative capabilities of the ReID model,making it more robust in challenging real-world scenarios.Extensive experiments were conducted on benchmark datasets to demonstrate the effectiveness of the proposed method,achieving state-of-the-art performance.