基于语义感知的行人重识别技术研究

Research on Pedestrian Re-Identification Technology Based on Semantic Perception

针对由于行人拍摄相机参数、拍摄环境以及角度等的差异,使行人重识别算法的准确率较低的问题,提出了一种基于行人语义感知信息以及深度学习的行人重新识别算法。首先,超分辨率重构行人视图,提升行人视图细节特征,提取行人的整体特征值,并用其识别体型差异较大的行人。其次,感知行人图像的语义信息,根据上述结果提取行人语义信息的特征值,用于识别体型相同或相似的行人。然后将行人视频中的人体宏观特征值以及语义感知的信息特征值融合为综合的特征值。使用生成的特征值计算与不同个体视频特征值的间距,识别海量人物图像。最后,在不同的数据集中验证了算法的性能。实验结果表明,该基于语言感知行人重识别算法的mAP和rand-1值最高。

Due to differences in camera parameters,shooting environment,and angles for pedestrian photography,the accuracy of pedestrian recognition algorithms still needs to be improved.To this end,a pedestrian re recognition algorithm based on pedestrian semantic perception information and deep learning is proposed.Firstly,super-resolution reconstruction of pedestrian views enhances the detailed features of pedestrian views,extracts the overall feature values of pedestrians,and uses them to identify pedestrians with significant body differences.Secondly,the Semantic information of pedestrian images is perceived,and the feature values of pedestrian Semantic information are extracted according to the above results to identify pedestrians with the same or similar body shape.Then,the macroscopic feature values of the human body and the semantic perception information feature values in the pedestrian video are fused into a comprehensive feature value.Use the generated feature values to calculate the distance between them and the video feature values of different individuals,and identify massive character images.Finally,this article validated the performance of the algorithm in different datasets.The experimental results show that the language perception based pedestrian recognition algorithm has the highest mAP and rand-1 values.