Abstract
Person re-identification can be a part of almost any multi-camera surveillance systems. Most previous works propose strategies for short-term person re-identification which are usually driven from appearance features of RGB images. However, when people appear in excessive lighting or change clothes (i.e. long-term case), short-term person re-identification approaches have a tendency to fail. In this paper, we propose a novel approach for long-term person re-identification by employing depth videos of RGB-D sensors. We also develop a sparse canonical correlation analysis using a local third-order tensor model to accomplish multi-level person re-identification. The tensor representations of images make the space for performing the multi-level person re-identification simpler compared to existing methods. Finally, we evaluate our experiments on RGB-D long-term datasets consisting of BIWI RGBD-ID dataset and IAS-Lab RGBD-ID dataset. The results demonstrate the efficiency of the proposed method compared to recent methods.
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References
Ahmed E, Jones M, Marks TK (2015) An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE international conference on computer vision and pattern recognition
An L, Yang S, Bhanu B (2015) Person re-identification by robust canonical correlation analysis. IEEE Signal Process Lett 22(8):1103–1107
An L, Kafai M, Yang S, Bhanu B (2016) Person re-identification with reference descriptor. IEEE Trans Circuits Syst Video Technol 26(4):776–787
An L, Chen X, Yang S, Bhanu B (2016) Sparse representation matching for person re-identification. Inf Sci 355:74–89
An L, Qin Z, Chen X, Yang S (2017) Multi-level common space learning for person re-identification. IEEE Trans Circuits Syst Video Technol. https://doi.org/10.1109/TCSVT.2017.2680118
Barbosa BI, Cristani M, Bue AD, Bazzani L, Murino V (2012) Re-identification with rgb-d sensors. In: First international workshop on re-identification. Springer, Berlin/Heidelberg, pp 433–442
Chahla C, Snoussi H, Abdallah F, Dornaika F (2017) Discriminant quaternion local binary pattern embedding for person reidentification through prototype formation and color categorization. Eng Appl Artif Intell 58:27–33
Chattopadhyay P, Sural S, Mukherjee J (2014) Frontal gait recognition from incomplete sequences using RGB-D camera. IEEE Trans Inf Forensics Secur 9(11):1843–1856
Chu H, Qi M, Liu H, Jiang J (2017) Local region partition for person re-identification. Multimed Tools Appl 78:27067–27083. https://doi.org/10.1007/s11042-017-4817-4
Dai J, Zhang Y, Lu H, Wang H (2018) Cross-view semantic projection learning for person re-identification. Pattern Recogn 75:63–76
Fang W, Hu HM, Hu Z, Liao S, Li B (2018) Perceptual hash-based feature description for person re-identification. Neurocomputing 272:520–531
Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2013) Symmetry-driven accumulation of local features for human characterization and re-identification. Comput Vis Image Underst 117(2):130–144
Fehr D, Cherian A, Sivalingam R, Nickolay S, Morellas V, Papanikolopoulos N (2012) Compact covariance descriptors in 3d point clouds for object recognition. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), pp 1793–1798
Figueira D, Bazzani L, Minh HQ, Cristani M, Bernardino A, Murino V (2013) Semi-supervised multi-feature learning for person re-identification. In: Proceedings of IEEE international conference on advanced video and signal based surveillance (AVSS), pp 111–116
Gao B, Zeng M, Xu S, Sun F, Guo J (2016) Person re-identification with discriminatively trained viewpoint invariant orthogonal dictionaries. Electron Lett 52(23):1914–1916
García J, Martinel N, Gardel A, Bravo I, Foresti GL, Micheloni C (2016) Modeling feature distances by orientation driven classifiers for person re-identification. J Vis Commun Image Represent 38:115–129
Geng Y, Hu HM, Zeng G, Zheng J (2015) A person re-identification algorithm by exploiting region-based feature salience. J Vis Commun Image Represent 29:89–102
Hu HM, Fang W, Zeng G, Hu Z, Li B (2017) A person re-identification algorithm based on pyramid color topology feature. Multimed Tools Appl 76(24):26633–26646
Imani Z, Soltanizadeh H (2016) Person reidentification using local pattern descriptors and anthropometric measures from videos of kinect sensor. IEEE Sensors J 16(16):6227–6238
Imani Z, Soltanizadeh H (2018) Histogram of the node strength and histogram of the edge weight: two new features for RGB-D person re-identification. Sci China Inf Sci 61(9):092108. https://doi.org/10.1007/s11432-016-9086-8
Imani Z, Soltanizadeh H (2019) Local binary pattern, local derivative pattern and skeleton features for RGB-D person re-identification. Natl Acad Sci Lett 42(3):233–238
Imani Z, Soltanizadeh H, & Orouji AA (2019) Short-Term Person Re-identification Using RGB, Depth and Skeleton Information of RGB-D Sensors. Iran J Sci Technol Trans Electr Eng https://doi.org/10.1007/s40998-019-00249-9
Khedher MI, El-Yacoubi MA, Dorizzi B (2017) Fusion of appearance and motion-based sparse representations for multi-shot person re-identification. Neurocomputing 248:94–104
Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500
Köstinger M, Hirzer M, Wohlhart P, Roth P, Bischof H (2012) Large scale metric learning from equivalence constraints. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2288–2295
Koukiou G, Anastassopoulos V (2018) Local difference patterns for drunk person identification. Multimed Tools Appl 77(8):9293–9305
Kviatkovsky I, Adam A, Rivlin E (2013) Color invariants for person reidentification. IEEE Trans Pattern Anal Mach Intell 35(7):1622–1634
Li W, Wu Y, Li J (2017) Re-identification by neighborhood structure metric learning. Pattern Recogn 61:327–338
Liao S, 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 (CVPR), pp 2197–2206
Liong VE, Lu J, Ge Y (2015) Regularized local metric learning for person re-identification. Pattern Recogn Lett 68:288–296
Liu Y, Lasang P, Siegel M, Sun Q (2015) Geodesic invariant feature: a local descriptor in depth. IEEE Trans Image Process 24(1):236–248
Liu H, Hu L, Ma L (2017) Online RGB-D person re-identification based on metric model update. CAAI Trans Intell Technol 2(1):48–55
Lu H, Plataniotis KN, Venetsanopoulos AN (2011) A survey of multilinear subspace learning for tensor data. Pattern Recogn 44:1540–1551
Ma B, Su Y, Jurie F (2014) Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vis Comput 32(6):379–390
Marcolin F, Vezzetti E (2017) Novel descriptors for geometrical 3D face analysis. Multimed Tools Appl 76(12):13805–13834
Martinel N, Das A, Micheloni C, Chowdhury AR (2015) Re-identification in the function space of feature warps. IEEE Trans Pattern Anal Mach Intell 37(8):1656–1669
Mignon A, Jurie F (2012) PCCA: a new approach for distance learning from sparse pairwise constraints. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2666–2672
Munaro M, Fossati A, Basso A, Menegatti E, Gool LV (2014) One-shot person re-identification with a consumer depth camera. In: Advances in computer vision and pattern recognition. Springer, London, pp 161–181
Munaro M, Ghidoni S, Dizmen DT, Menegatti E (2014) A feature-based approach to people re-identification using skeleton keypoints. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), pp 5644–5651
Nanni L, Munaro M, Ghidoni S, Menegatti E, Brahnam S (2016) Ensemble of different approaches for a reliable person re-identification system. Appl Comput Inform 12(2):142–153
Pala F, Satta R, Fumera G, Roli F (2016) Multi-modal person re-identification using RGB-D cameras. IEEE Trans Circuits Syst Video Technol 26(4):788–799
Patruno C, Marani R, Cicirelli G, Stella E, D'Orazio T (2019) People re-identification using skeleton standard posture and color descriptors from RGB-D data. Pattern Recogn 89:77–90
Pedagadi S, Orwell J, Velastin S, Boghossian B (2013) Local Fisher discriminant analysis for pedestrian re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3318–3325
Prosser B, Zheng WS, Gong S, Xiang T (2010) Person reidentification by support vector ranking. In: Proceedings of the British machine vision conference (BMVC), pp 21.1–21.11
Rahmawati E, Listyasari M, Aziz AS, Sukaridhoto S, Damastuti FA, Bachtiar MM, Sudarsono A (2017) Digital signature on file using biometric fingerprint with fingerprint sensor on smartphone. In: International electronics symposium on engineering technology and applications (IES-ETA), pp 234–238
Ren L, Lu J, Feng J, Zhou J (2017) Multi-modal uniform deep learning for RGB-D person re-identification. Pattern Recogn 72:446–457
Skelly LJ, Sclaroff S (2007) Improved feature descriptors for 3d surface matching. In: Proceedings of the society of photo-optical instrumentation engineers (SPIE), pp 67 620A–67 620A
Vezzetti E, Marcolin F, Stola V (2013) 3D human face soft tissues landmarking method: an advanced approach. Comput Ind 64(9):1326–1354
Wang Z, Hu R, Liang C, Yu Y (2016) Zero-shot person re-identification via cross-view consistency. IEEE Trans Multimed 18(2):260–272
Wang J, Wang Z, Liang C, Gao C, Sang N (2018) Equidistance constrained metric learning for person re-identification. Pattern Recogn 74:38–51
Wu A, Zheng WS, Lai J (2017) Robust depth-based person re-identification. IEEE Trans Image Process 26(6):2588–2603
Xu X, Li W, Xu D (2015) Distance metric learning using privileged information for face verification and person re-identification. IEEE Trans Neural Netw Learn Syst 26(12):3150–3162
Xu Y, Guo J, Huang Z, Qiu W (2018) Sparse coding with cross-view invariant dictionaries for person re-identification. Multimed Tools Appl 77(9):10715–10732
Yan J, Zheng W, Zhou X, Zhao Z (2012) Sparse 2-d canonical correlation analysis via low rank matrix approximation for feature extraction. IEEE Signal Process Lett 19(1):51–54
Yan C, Luo M, Liu W, Zheng Q (2018) Robust dictionary learning with graph regularization for unsupervised person re-identification. Multimed Tools Appl 77(3):3553–3577
Yang Z, Hu X, Dai F, Pang J, Jiang T, Tao D (2018) Person re-identification by discriminant analytical least squares metric learning. Mach Vis Appl 29:1019–1031. https://doi.org/10.1007/s00138-018-0917-z
Zhao C, Wang X, Miao D, Wang H, Zheng W, Xu Y, Zhang D (2018) Maximal granularity structure and generalized multi-view discriminant analysis for person re-identification. Pattern Recogn 79:79–96
Zhao C, Chen Y, Wang X, Wong WK, Miao D, Lei J (2018) Kernelized random KISS metric learning for person re-identification. Neurocomputing 275:403–417
Zhao C, Chen Y, Wei Z, Miao D, Gu X (2018) QRKISS: a two-stage metric learning via QR-decomposition and KISS for person re-identification. Neural Process Lett 49:899–922. https://doi.org/10.1007/s11063-018-9820-x
Zheng WS, Gong S, Xiang T (2013) Reidentification by relative distance comparison. IEEE Trans Pattern Anal Mach Intell 35(3):653–668
Zhou Q, Zheng S, Ling H, Su H, Wu S (2017) Joint dictionary and metric learning for person re-identification. Pattern Recogn 72:196–206
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Imani, Z., Soltanizadeh, H. & Orouji, A.A. Tensor-based sparse canonical correlation analysis via low rank matrix approximation for RGB-D long-term person re-identification. Multimed Tools Appl 79, 11787–11811 (2020). https://doi.org/10.1007/s11042-019-08311-8
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DOI: https://doi.org/10.1007/s11042-019-08311-8