Abstract Due to variations in pose and illumination condition, the appearance of a person can be significantly different in two views and therefore the performance of person re identification is degraded. In this paper, a feature fusion method for person re identification is proposed including HSV color feature， color histogram feature and texture feature extracted by the histogram of oriented gradient descriptor. The specific process is divided into the training phase and the recognition phase. In the training phase, the feature descriptors of each image in the reference dataset are firstly extracted, and then a correlation matrix of the image features from two cameras is learned using canonical correlation analysis. As for re identification, the feature descriptors of each image in the gallery dataset and the probe dataset are firstly extracted, and then they are transformed by the correlation matrix. Finally， re identification is implemented by measuring the similarity between the gallery image descriptor and the probe image descriptor. Experimental results on three datasets show that the proposed method outperforms the state of art approaches.
Fund:Supported by National Natural Science Foundation of China(No.61300075)
About author:: YUAN Li(Corresponding author), born in 1978, Ph.D., associate professor. Her research interests include pattern recognition and image processing.
.
TIAN Ziru, born in 1990, master student. Her research interests include pattern recognition and image processing.
[1] BEDAGKAR GALA A, SHAH S K. Editor′s Choice Article: A Survey of Approaches and Trends in Person Re identification. Image and Vision Computing, 2014, 32(4): 270-286. [2] SAGHAFI M A, HUSSAIN A, SAAD M H M, et al. Appearance Based Methods in Re identification: A Brief Review // Proc of the 8th IEEE International Colloquium on Signal Processing and ITS Applications. Washington, USA: IEEE, 2012: 404-408. [3] XIANG J P. Active Learning for Person Re identification // Proc of the International Conference on Machine Learning and Cybernetics. Washington, USA: IEEE, 2012, I: 336-340. [4] BEDAGKAR GALA A, SHAH S K. Part Based Spatio Temporal Model for Multi person Re identification. Pattern Recognition Le tters, 2012, 33(14): 1908-1915. [5] MAZZON R, TAHIR S F, CAVALLARO A. Person Re identification in Crowd. Pattern Recognition Letters, 2012, 33(14): 1828-1837. [6] BAK S, CORVEE E, BREMOND F, et al. Person Re identification Using Spatial Covariance Regions of Human Body Parts // Proc of the 7th IEEE International Conference on Advanced Video and Signal Based Surveillance. Washington, USA: IEEE, 2010: 435-440. [7] MANJUNATH B S, OHM J R, VASUDEVAN V V. Color and Texture Descriptors. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(6): 703-715. [8] IODICE S, PETROSINO A. Salient Feature Based Graph Matching for Person Re identification. Pattern Recognition, 2015, 48(4): 1074-1085. [9] GENG Y B, HU H M, ZENG G D, et al. A Person Re identification Algorithm by Exploiting Region Based Feature Salience. Journal of Visual Communication and Image Representation, 2015, 29: 89-102. [10] LIU Z, ZHANG Z X, WU Q, et al. Enhancing Person Re identification by Integrating Gait Biometric. Neurocomputing, 2015, 168: 1144-1156. [11] FARENZENA M, BAZZANI L, PERINA A, et al. Person Re identification by Symmetry Driven Accumulation of Local Features // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2010: 2360-2367. [12] ZHENG W S, GONG S G, XIANG T. Reidentification by Relative Distance Comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(3): 653-668. [13] DING S Y, LIN L, WANG G R, et al. Deep Feature Learning with Relative Distance Comparison for Person Re identification. Pattern Recognition, 2015, 48(10): 2993-3003. [14] ZHENG W S, GONG S F, XIANG T. Towards Open World Person Re identification by One Shot Group Based Verification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(3): 591-606. [15] AN L, KAFAI M, YANG S F, et al. Reference Based Person Re identification // Proc of the 10th IEEE International Conference on Advanced Video and Signal Based Surveillance. Washington, USA: IEEE, 2013: 244-249. [16] HARDOON D R, SZEDMAK S, SHAWE TAYLOR J. Canonical Correlation Analysis: An Overview with Application to Learning Methods. Neural Computation, 2004, 16(12): 2639-2664. [17] MARTINEL N, DAS A, MICHELONI C, et al. Re identification in the Function Space of Feature Warps. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(8): 1656-1669. [18] LISANTI G, MASI I, BAGDANOV A D, et al . Person Re identification by Iterative Re Weighted Sparse Ranking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(8): 1629-1642. [19] AVRAHAM T, GURVICH I, LINDENBAUM M, et al . Learning Implicit Transfer for Person Re identification // Proc of the 10th European Conference on Computer Vision. Berlin, Germany: Springer Verlag, 2012, II: 381-390. [20] WEINBERGER K Q, SAUL L K. Distance Metric Learning for Large〖JP〗 Margin Nearest Neighbor Classification. Journal of Machine Lear ning Research, 2009, 10: 207-244. [21] ZHENG W S, GONG S G, XIANG T. Person Re identification by Probabilistic Relative Distance Comparison // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2011: 649-656. [22] BAZZANI L, CRISTANI M, MURINO V. Symmetry Driven Accumulation of Local Features for Human Characterization and Re identification . Computer Vision and Image Understanding, 2013, 117(2): 130-144. [23] K STINGER M, HIRZER M, WOHLHART P, et al . Large Scale Metric Learning from Equivalence Constraints // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2012: 2288-2295. [24] ZHAO R, OUYANG W L, WANG X G. Unsupervised Salience Learning for Person Re identification // Proc of the IEEE Confe rence on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2013: 3586-3593.
2017, Vol. 30 
