基于难样本混淆增强特征鲁棒性的行人重识别

Person Re-identification Based on Hard Negative Sample Confusion to Enhance Robustness of Features

随着深度学习的兴起,行人重识别逐渐成为计算机领域的热门话题。它通过给定的查询行人图像进行跨摄像机检索,找出与查询身份相匹配的行人。然而,由于受到不同视角下的背景、光照等因素影响,采集到的行人图像中存在大量的难样本,利用这些难样本训练得到的模型识别性能低下,缺乏鲁棒性。因此,为了提高模型对难样本的鉴别能力,设计了一种新颖的通过混淆因子合成具有难样本信息图像的方法。对于每批输入图片,通过相似性度量寻找每张图像对应的难样本,结合混淆因子合成具有难样本信息的新图像再以有监督的方式促使模型挖掘难样本信息,从而提高模型鲁棒性。大量对比实验表明,所提方法在主流数据集上达到了较高的识别率,消融实验证明了所提方法的有效性。

With the rise of deep learning,person re-identification has gradually become a hot topic in the computer vision field.It performs cross-camera retrieval through a given query image,and finds the images that match the query identity.However,due to the factors such as background and illumination under different cameras,there are a large number of hard negative samples in the collected pedestrian datasets,and the performance of the model trained using these samples is bad and lacks robustness.Therefore,in order to improve the ability of the model to discriminate such negative samples,a novel method of synthesizing images with hard negative samples information through confusion factors is designed.For each input batch images,the similarity measurement is used to find the hard negative sample corresponding to each image,the new generated images with the clues of negative samples are synthesized through the confusion factor,and the model is prompted to mine the negative samples information in a supervised manner thus improving the model robustness.A large number of comparative experiments show that the proposed method achieves high performance on the mainstream datasets.The ablation study proves the effectiveness of the proposed method.