Research on Pedestrian Re-Identification Using CNN Feature and Pedestrian Combination Attribute

Aiming at the problem that the existing pedestrian recognition technology re-identification effect is not good and the traditional method has low recognition effect. A feature fusion network is proposed in this paper, which combines the CNN features extracted by ResNet with the manual annotation attributes into a unified feature space. ResNet solved the problem of network degradation and multi-convergence in multi-layer CNN training, and extracted deeper features. The attribute combination method was adopted by the artificial annotation attributes. The CNN features were constrained by the hand-crafted features because of the back propagation. Then the loss measurement function was used to optimize network identification results. In the public datasets VIPeR, PRID, and CUHK for further testing, the experimental results show that the method achieves a high cumulative matching score.

DAAPS: A Deformable-Attention-Based Anchor-Free Person Search Model

Person Search is a task involving pedestrian detection and person re-identification,aiming to retrieve person images matching a given objective attribute from a large-scale image library.The Person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively.The current popular Person Search models,whether end-to-end or two-step,are based on anchor boxes.However,due to the limitations of the anchor itself,the model inevitably has some disadvantages,such as unbalance of positive and negative samples and redundant calculation,which will affect the performance of models.To address the problem of fine-grained understanding of target pedestrians in complex scenes and small sizes,this paper proposes a Deformable-Attention-based Anchor-free Person Search model(DAAPS).Fully Convolutional One-Stage(FCOS),as a classic Anchor-free detector,is chosen as the model’s infrastructure.The DAAPS model is the first to combine the Anchor-free Person Search model with Deformable Attention Mechanism,applied to guide the model adaptively adjust the perceptual.The Deformable Attention Mechanism is used to help the model focus on the critical information and effectively improve the poor accuracy caused by the absence of anchor boxes.The experiment proves the adaptability of the Attention mechanism to the Anchor-free model.Besides,with an improved ResNeXt+network frame,the DAAPS model selects the Triplet-based Online Instance Matching(TOIM)Loss function to achieve a more precise end-to-end Person Search task.Simulation experiments demonstrate that the proposed model has higher accuracy and better robustness than most Person Search models,reaching 95.0%of mean Average Precision(mAP)and 95.6%of Top-1 on the CUHK-SYSU dataset,48.6%of mAP and 84.7%of Top-1 on the Person Re-identification in the Wild(PRW)dataset,respectively.

损坏图像下基于风格归一化与全局注意力的行人重识别

针对当前网络难以应对各种损坏类型的行人图像与易丢失跨维信息的问题,提出了一种损坏图像下基于风格归一化与全局注意力的行人重识别(pedestrain re-identification,ReID)方法。该方法通过平滑极大单元的风格归一化与恢复(smooth maximum unit-style normalization and restitution,SM-SNR)模块中的实例规范化(instance normalization,IN)过滤掉域中的风格变化,同时平滑极大单元(smooth maximum unit,SMU)能使该模块更充分地从删除的信息中提取行人相关特征并将其恢复至网络中,缓解损坏图像带来的风格差异。此外,全局注意力机制(global attention mechanism,GAM)通过关注通道与空间之间的相互作用,以捕获3个维度上的显著行人特征,减少跨维信息的丢失,最终使本模型在面对行人损坏图像时的识别能力得到有效提高,且保留了在干净数据集上的竞争力。实验结果表明,本算法在损坏测试集上的各项指标与目前主流算法对比具有显著的优越性。其中,本模型与2021年的CIL模型使用CUHK03数据集比较的结果为:在Corrupted Eval上,R-1、mAP和mINP分别提高了15.18%、15.75%与11.65%;在Clean Eval上,R-1与mINP仅降低了0.24%、0.75%,mAP提升了0.25%。