Novel Similarity Metric Learning Using Deep Learning and Root SIFT for Person Re-identification

This paper deals with the person re-identification, intending to match the images of the person captured using disjoint cameras mounted in different locations. Such a task of matching the images remains a difficult issue as the appearance of the individual differs from the perspective of the various cameras. Inspired by the recent success of deep learning in the domain of person re-identification, a novel deep learning framework which combines deep features and Root Scale Invariant Features Transform (Root SIFT) features has been proposed. The conventional deep Convolutional Neural Network (CNN) can obtain significant features but does not take into account the spatial relationship between the features. Also, CNN requires an enormous number of instances to train the network. To address these issues, the proposed method combines Root SIFT features along with the CNN features. With the combination of Deep and Root SIFT features, the model can give improved performance over other CNN based models. Experiments were conducted on standard datasets CUHK 03 (labelled and detected), CUHK 01 and VIPeR and the matching rate is reported as 74.45% for CUHK 03 (labelled), 72.63% for CUHK 03 (detected), 76.12% for CUHK 01 and 48.45% for VIPeR dataset. The experiments demonstrate that the proposed algorithm has improved identification rate over the recent algorithms.

[1]  Kai Xie,et al.  Face Recognition Using the SR-CNN Model , 2018, Sensors.

[2]  Xiaogang Wang,et al.  DeepReID: Deep Filter Pairing Neural Network for Person Re-identification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Lei Zhang,et al.  View Confusion Feature Learning for Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[4]  Xiaogang Wang,et al.  Human Reidentification with Transferred Metric Learning , 2012, ACCV.

[5]  Qingming Leng.  Co-Metric Learning for Person Re-Identification , 2018, Adv. Multim..

[6]  Meng Wang,et al.  Person Re-Identification With Metric Learning Using Privileged Information , 2018, IEEE Transactions on Image Processing.

[7]  Shengcai Liao,et al.  Person re-identification by Local Maximal Occurrence representation and metric learning , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Theo Gevers,et al.  Color Constancy for Multiple Light Sources , 2012, IEEE Transactions on Image Processing.

[9]  Sameh Khamis,et al.  Person re-identification using semantic color names and RankBoost , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[10]  Yinghuan Shi,et al.  GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification , 2019, ArXiv.

[11]  Ziyan Wu,et al.  Re-Identification With Consistent Attentive Siamese Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Yann LeCun,et al.  Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[14]  Shengcai Liao,et al.  Deep Metric Learning for Person Re-identification , 2014, 2014 22nd International Conference on Pattern Recognition.

[15]  Shaogang Gong,et al.  Reidentification by Relative Distance Comparison , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Xiaogang Wang,et al.  Unsupervised Salience Learning for Person Re-identification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Jiwen Lu,et al.  Learning Invariant Color Features for Person Reidentification , 2014, IEEE Transactions on Image Processing.

[18]  Nikita Jadhav,et al.  LEARNING INVARIANT COLOUR FEATURES FOR PERSON REIDENTIFICATION , 2020 .

[19]  Da-Xiang Li,et al.  Learning Large Margin Multiple Granularity Features with an Improved Siamese Network for Person Re-Identification , 2020, Symmetry.

[20]  Tao Xiang,et al.  Multi-scale Deep Learning Architectures for Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[21]  Qi Tian,et al.  Scalable Person Re-identification: A Benchmark , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[22]  Mubarak Shah,et al.  Appearance modeling for tracking in multiple non-overlapping cameras , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[23]  Alessandro Perina,et al.  Person re-identification by symmetry-driven accumulation of local features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[24]  Qing Liu,et al.  Multi-Information Flow CNN and Attribute-Aided Reranking for Person Reidentification , 2019, Comput. Intell. Neurosci..

[25]  Horst Bischof,et al.  Dense appearance modeling and efficient learning of camera transitions for person re-identification , 2012, 2012 19th IEEE International Conference on Image Processing.

[26]  Hichem Snoussi,et al.  Discriminant quaternion local binary pattern embedding for person re-identification through prototype formation and color categorization , 2017, Eng. Appl. Artif. Intell..

[27]  Horst Bischof,et al.  Person Re-identification by Descriptive and Discriminative Classification , 2011, SCIA.

[28]  Andrew Zisserman,et al.  Three things everyone should know to improve object retrieval , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Ehud Rivlin,et al.  Color Invariants for Person Reidentification , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Dacheng Tao,et al.  Person Re-Identification With Metric Learning Using Privileged Information. , 2018, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[31]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[32]  Horst Bischof,et al.  Large scale metric learning from equivalence constraints , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Peng Li,et al.  Person re-identification using color enhancing feature , 2015, 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR).

[34]  Huixiang Qiao,et al.  Person Re-Identification via Group Symmetry Theory , 2019, IEEE Access.

[35]  Slawomir Bak,et al.  Person Re-identification Using Haar-based and DCD-based Signature , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[36]  Michael Jones,et al.  An improved deep learning architecture for person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Xiaogang Wang,et al.  Person Re-identification by Salience Matching , 2013, 2013 IEEE International Conference on Computer Vision.

[38]  Fatih Murat Porikli,et al.  Inter-camera color calibration by correlation model function , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).