Abstract
Person re-identification is a challenging problem in computer vision. Lots of research interest is observed in this area over the past few years. A model for complete person re-identification can prove useful in this direction. Use of convolutional neural networks for pedestrian detection can improve the accuracy of detection to a larger extent. Deriving a descriptor which is invariant to the changes in the illumination, background and the pose can make the difference in the re-identification process. The predominant part of our work focuses on building a robust descriptor which can tackle such challenges. We have concentrated on building a descriptor by employing appearance-based features extracted both at local and global levels. Further, the dimensionality of the descriptor is reduced using kernel PCA. Distance metric learning algorithms are used to evaluate the descriptor on three major benchmark datasets. We propose a complete person re-identification system which involves both pedestrian detection and person re-identification. Major contributions of this work are to detect pedestrians from surveillance videos using CNN-based learning and to generate a kernel-PCA-based spatial descriptor and evaluate the descriptor using known distance metric learning methods on benchmark datasets.
Similar content being viewed by others
References
Papageorgiou CP, Oren M, Poggio T (1998) A general framework for object detection. In: Sixth international conference on computer vision, pp 555–562
Viola P, Jones M, Snow D (2003) Detecting pedestrians using patterns of motion and appearance. In: International conference on computer vision (ICCV)
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: CVPR, pp 886–893
Zhu Q, Avidan S, Yeh M, Cheng K (2006) Fast human detection using a cascade of histograms of oriented gradients. In: Proceedings of IEEE conference of computer vision and pattern recognition
Sabzmeydani P, Mori G (2007) Detecting pedestrians by learning shapelet features. In: IEEE conference on computer vision and pattern recognition, pp 1–8
Felzenszwalb PF, McAllester DA, Ramanan D (2008) A discriminatively trained, multiscale, deformable part model. In: IEEE conference on computer vision and pattern recognition, pp 1–8
Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Sermanet P, Kavukcuoglu K, Chintala S, LeCun Y (2013) Pedestrian detection with unsupervised multi-stage feature learning. In: IEEE conference on computer vision and pattern recognition, pp 3626–3633
Ouyang W, Wang X (2013) Joint deep learning for pedestrian detection. In: IEEE international conference on computer vision, pp 2056–2063
Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: IEEE conference on computer vision and pattern recognition
Bazzani L, Cristani M, Perina A, Farenzena M, Murino V (2010) Multiple-shot person re-identification by hpe signature. In: 20th international conference on pattern recognition (ICPR)
Cheng DS, Cristani M, Stoppa M, Bazzani L, Murino V (2011) Custom pictorial structures for re-identification. In: British machine vision conference
Bak S, Corvee E, Bremond F, Thonnat M (2010) Person re-identification using haar-based and dcd-based signature. In: Proceedings of 7th IEEE international conference on advanced video and signal based surveillance (AVSS)
Štruc V, Pavecšić N (2009) Gabor-based Kernel partial-least-squares discrimination features for face recognition. Informatica (Vilnius) 20(1):115–138
Štruc V, Pavecšić N (2010) The complete Gabor-Fisher classifier for robust face recognition. EURASIP Adv Signal Process 2010:26
Shengcai L, Hu Y, Zhu X, Li SZ (2015) Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2197–2206
Bingpeng MA, Su Y, Jurie F (2012) Bicov: a novel image representation for person re-identification and face verification. In: British machine vision conference
Weber M, Buml M, Stiefelhagen R (2011) Part-based clothing segmentation for person retrieval. In: 8th IEEE international conference on advanced video and signal based surveillance, AVSS, pp 361–366
Hirzer M, Roth PM, Bischof H (2012) Person re-identification by efficient metric learning. In: Proceedings of IEEE international conference on advanced video and signal-based surveillance
Ijiri Y, Lao S (2012) Human re-identification through distance metric learning based on jensen-shannon kernel. In: International conference on computer vision theory and applications, pp 603–612
Pedagadi S, Orwell J, Velastin S, Boghossian B (2013) Local fisher discriminant analysis for pedestrian re-identification. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 3318–3325
Xiang ZJ, Chen Q, Liu Y (2014) Person re-identification by fuzzy space color histogram. Multimed Tools Appl 73(1):91–107
Leng Q, Hu R, Liang C, Wang Y, Chen J (2015) Person re-identification with content and context re-ranking. Multimed Tools Appl 74(17):6989–7014
Bazzani L, Cristani M, Perina A, Murino V (2011) Multipleshot person re-identification by chromatic and epitomic analyses. Pattern Recognit Lett 33(7):898–903
Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE international workshop on performance evaluation of tracking and surveillance (PETS)
Guillaumin M, Verbeek J, Schmid C (2009) Is that you? Metric learning approaches for face identification. In: Proceedings of IEEE international conference on computer vision
Davis JV, Kulis B, Jain P, Sra S, Dhillon IS (2007) Information-theoretic metric learning. In: Proceedings of international conference machine learning
Weinberger KQ, Saul LK (2008) Fast solvers and efficient implementations for distance metric learning. In: Proceedings of international conference on machine learning
Ding S, Lin L, Wang G, Chao H (2015) Deep feature learning with relative distance comparison for person re-identification. Pattern Recognit 48(10):2993–3003
Ahmed E, Jones M, Marks TK (2015) An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3908–3916
Yi D, Lei Z, Liao S, Li SZ (2014) Deep metric learning for person re-identification. In: Proceedings of the 2014 22nd international conference on pattern recognition, pp 34–39
Sathish PK, Balaji S (2017) Multi-frame twin-channel descriptor for person re-identification in real-time surveillance videos. Int J Multimed Inf Retr 6:289–294
Kaewtrakulpong P, Bowden R (2001) An improved adaptive background mixture model for realtime tracking with shadow detection. In: Proceedings of 2nd European workshop on advanced video based surveillance systems, AVBS01, video based surveillance systems: computer vision and distributed processing
Welch G, Bishop G (2001) An introduction to the kalman filter. An introduction to the kalman filter. In: ACM SIGGRAPH international conference on computer graphics and interactive techniques
Girshick R, Donahue J, Darrell T, Malik J (2016) Region-based convolutional networks for accurate object detection and semantic segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142–158
Razavian AS, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding base line for recognition. In: Proceedings of IEEE conference on computer vision pattern recognition workshops, pp 512–519
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298
Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database. In: Proceedings of NIPS, pp 487–495
Baltieri D, Vezzani R, Cucchiara R (2011) 3DPeS: 3d people dataset for surveillance and forensics. In: Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Parkhi OM, Vedaldi A, Zisserman A, Jawahar CV (2012) Cats and Dogs. In: IEEE conference on computer vision and pattern recognition
Wang X, Ma X, Grimson E (2009) Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models. IEEE Trans Pattern Anal Mach Intell (PAMI) 31:539–555
Lowe D (2004) Distinctive image features from scale invariant features. Int J Comput Vis 60:91–110
Vedaldi A, Fulkerson B (2008) VLFeat: an open and portable library of computer vision algorithms. http://www.vlfeat.org
Scholkopf B, Smola A, Muller K-R (1999) Kernel principal component analysis. Advances in Kernel methods: support vector learning. MIT Press, Cambridge, pp 327–352
Roth PM, Hirzer M, Kstinger M, Beleznai C, Bischof H (2014) Mahalanobis distance learning for person re-identification. Springer, Berlin, pp 247–267
Hirzer M, Beleznai Csaba, Roth Peter M, Bischof Horst (2011) Person re-identification by descriptive and discriminative classification. In: Proceedings of Scandinavian conference on image analysis (SCIA)
Zheng WS, Gong S, Xiang T (2009) Associating groups of people. In: British machine vision conference
Jojic N, Perina A, Cristani M, Murino V, Frey B (2009) Stel component analysis: modeling spatial correlations in image class structure. In: IEEE conference on computer vision and pattern recognition, pp 2044–2051
Mignon A, Jurie F (2012) PCCA: a new approach for distance learning from sparse pairwise constraints. In: IEEE conference on computer vision and pattern recognition, pp 2666–2672
K̈ostinger M, Hirzer M, Wohlhart P, Roth PM, Bischof H (2012) Large scale metric learning from equivalence constraints. In: Proceedings of IEEE conference on computer vision and pattern recognition
Zhao R, Ouyang W, Wang X (2013) Person re-identification by salience matching. In: IEEE international conference on computer vision, pp 2528–2535
Cheng D, Gong Y, Zhou S, Wang J, Zheng N (2016) Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: The IEEE conference on computer vision and pattern recognition (CVPR)
Zhao R, Ouyang W, Wang X (2013) Unsupervised salience learning for person re-identification. In: IEEE conference on computer vision and pattern recognition, pp 3586–3593
Li Z, Chang S, Liang F, Huang TS, Cao L, Smith JR (2013) Learning locally-adaptive decision functions for person verification. In: IEEE conference on computer vision and pattern recognition, pp 3610–3617
Karanam S, Li Y, Radke R (2015) Sparse re-id: block sparsity for person re-identification. In: Computer Vision and Pattern Recognition workshop
Karanam S, Li Y, Radke RJ (2015) Person re-identification with discriminatively trained viewpoint invariant dictionaries. In: IEEE international conference on computer vision, pp 4516–4524
You J, Wu A, Li X, Zheng WS (2016) Top-push video-based person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1345–1353
Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645
Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Proceedings of the 10th European conference on computer vision
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115:211–252
Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: IEEE Computer society conference on computer vision and pattern recognition, vol 2, pp 2246–252
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sathish, P.K., Balaji, S. A complete person re-identification model using Kernel-PCA-based Gabor-filtered hybrid descriptors. Int J Multimed Info Retr 7, 221–229 (2018). https://doi.org/10.1007/s13735-018-0153-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13735-018-0153-3