摘要
为了增强行人重识别模型(Re-identification,ReID)的遮挡感知和局部特征捕捉能力,提出一种基于特征融合的局部表征学习的方法.首先,设计遮挡样本扩充策略,通过模拟多样化的遮挡场景,有效提升模型的鲁棒性和遮挡感知能力.其次,引入局部层次编码器,在全局语义的指导下提取序列的空间相关性特征,从而增强局部特征的可鉴别性和语义完整性.实验结果显示,在Occluded-Duke和Market-1501数据集上,该方法表现出色,特别是在Occluded-Duke数据集上的rank-1达到69.2%,优于现有先进方法,提升幅度为1.3百分点,验证了该方法在提升行人重识别任务性能方面的有效性.
Abstract
To enhance the occlusion perception and local feature capturing ability of pedestrian re-identification(ReID)models,this paper proposes a method of local representation learning based on feature fusion.First,an Occlusion Sample Expansion Strategy(OSES)is designed to effectively improve the model's robustness and occlusion perception ability by simulating diverse occlusion scenarios.Second,a local hierarchical encoder is introduced to extract spatial correlation features of the sequence under the guidance of global semantics,thereby enhancing the discriminability and semantic integrity of local features.Experimental results on the Occluded-Duke and Market-1501 datasets demonstrate the effectiveness of the method.The rank-1 on the Occluded-Duke dataset reaches 69.2%,which outperforms the existing state-of-the-art methods by 1.3 percentage points and improves the performance of the re-recognition task.
NETL
NSTL
万方数据