计算机科学 ›› 2020, Vol. 47 ›› Issue (6): 164-169.doi: 10.11896/jsjkx.190500013
裴嘉震, 徐曾春, 胡平
PEI Jia-zhen, XU Zeng-chun, HU Ping
摘要： 行人再识别是视频监控中一项极具挑战性的任务。图像中的遮挡、光照、姿态、视角等因素,会对行人再识别的准确率造成极大影响。为了提高行人再识别的准确率,提出一种融合视点机制与姿态估计的行人再识别方法。首先,采用姿态估计算法Openpose定位行人关节点;然后,对行人图像进行视图判别以获得视点信息,并根据视点信息与行人关节点位置进行局部区域推荐,生成行人局部图像;接着,将全局图像与局部图像同时输入CNN提取特征;最后,采用特征融合网络将全局与局部的特征融合,以获取更具鲁棒性的特征表示。实验结果表明:提出的方法具有更高的行人再识别准确率,其在CHUK03数据集上的rank1达到了71.3%,在Market1501和DukeMTMC-reID数据集上的mAP分别达到了63.2%与60.5%。因此,所提方法能够很好地应对行人姿态变化和视角变化等问题。
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| [1]ZHENG L,SHEN L,TIAN L,et al.Scalable Person Re-identification:A Benchmark[C]//2015 IEEE International Conference on Computer Vision (ICCV).IEEE Computer Society,2015:1116-1124. [2]LI W,ZHAO R,XIAO T,et al.DeepReID:Deep Filter Pairing Neural Network for Person Re-identification[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE Computer Society,2014:152-159. [3]RISTANI E,SOLERA F,ZOU R,et al.Performance measures and a data set for multi-target,multi-camera Tracking[C]//European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking.2016:17-35. [4]LIAO S,HU Y,ZHU N X,et al.Person re-identification by Local Maximal Occurrence representation and metric learning[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE Computer Society,2015:2197-2206. [5]JOSE C,FLEURET F.Scalable metric learning via weighted approximate rank component analysis[C]//European Conference on Computer Vision.Cham:Springer,2016:875-890. [6]LIAO S,LI S Z.Efficient PSD Constrained Asymmetric Metric Learning for Person Re-identification[C]//2015 IEEE International Conference on Computer Vision (ICCV).IEEE Computer Society,2015:3685-3693. [7]YI D,LEI Z,LIAO S,et al.Deep Metric Learning for Person Re-identification[C]//2014 22nd International Conference on Pattern Recognition (ICPR).IEEE Computer Society,2014:34-39. [8]VARIOR R R,SHUAI B,LU J W,et al.A siamese long short-term memory architecture for human re-identification[C]//European Conference on Computer Vision.Cham:Springer,2016:135-153. [9]VARIOR R R,HALOI M,WANG G.Gated siamese convolutional neural network architecture for human re-identification[C]//European Conference on Computer Vision.Cham:Springer,2016:791-808. [10]DING S,LIN L,WANG G,et al.Deep feature learning with relative distance comparison for person re-identification[J].Pattern Recognition,2015,48(10):2993-3003. [11]WANG F,ZUO W,LIN L,et al.Joint learning of single-image and cross-image representations for person re-identification[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE Computer Society,2016:1288-1296. [12]ZHENG L,HUANG Y,LU H,et al.Pose Invariant Embedding for Deep Person Re-identification[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE Computer Society,2017:1456-1465. [13]ZHAO H,TIAN M,SUN S,et al.Spindle Net:Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE Computer Society,2017:1077-1085. [14]WANG H,LIU Z F,FANG B F,et al.Human pose estimation based on constellation tree structure appearance model[J].Computer Science,2014,41(3):76-79. [15]LI D,ZHANG Z,CHEN X,et al.A Richly Annotated Dataset for Pedestrian Attribute Recognition[C]//2016 IEEEConfe-rence on Computer Vision and Pattern Recognition (CVPR).IEEE Computer Society,2016. [16]CAO Z,SIMON T,WEI S E,et al.Realtime multi-person 2D pose estimation using part affinity fields[C]//IEEE Conference on Computer Vision & Pattern Recognition.IEEE,2017:1302-1310. [17]ZHANG L,XIANG T,GONG S.Learning a Discriminative Null Space for Person Re-identification[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2016:1239-1248. [18]MATSUKAWA T,OKABE T,SUZUKI E,et al.Hierarchical Gaussian Descriptor for Person Re-identification[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2016:1363-1372. [19]SCHUMANN A,STIEFELHAGEN R.Person Re-identification by Deep Learning Attribute-Complementary Information[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).IEEE Computer Society,2017:1435-1443. [20]ZHENG Z,ZHENG L,YANG Y.Unlabeled samples generate d by GAN improve the person re-identification baseline in vitro[C]//IEEE International Conference on Computer Vision.IEEE Computer Society,2017:3774-3782. [21]SUN Y,ZHENG L,DENG W,et al.SVDNet for Pedestrian Retrieval[C]//IEEE International Conference on Computer Vision.Piscataway:IEEE,2017:3820-3828. |
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