Abstract
Low illumination is a common problem for recognition and tracking. Low illumination video-based person re identification (re-id) is an important application in practice. Low illumination usually results in severe loss of visual appearance and space-time information contained in pedestrian image or video, which brings large difficulty to re-identification. However, the problem of low illumination video-based person re-id (LIVPR) has not been well studied. In this paper, we propose a novel triplet-based manifold discriminative distance learning (TMD2L) approach for LIVPR. By regarding each video as an image set, TMD2L aims to learn a manifold-based distance metric, under which the intrinsic structure of image sets can be preserved, and the distance between truly matching sets is smaller than that between wrong matching sets. Experiment results on the new collected low illumination person sequence (LIPS) dataset, as well as two simulated datasets LI-PRID 2011 and LI-iLIDS-VID show that our proposed approach TMD2L outperforms existing representative person re-id methods.
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References
Chen J, Wang Y, Tang YY (2016) Person re-identification by exploiting spatio-temporal cues and multi-view metric learning. IEEE Signal Process Lett 23 (7):998–1002
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 (CVPR), pp 2360–2367
Gong S, Cristani M, Yan S, Loy CC (2014) Person re-identification. Springer
Hirzer M, Beleznai C, Roth PM, Bischof H (2011) Person re-identification by descriptive and discriminative classification. In: Scandinavian conference on image analysis, pp 91–102
Hirzer M, Roth P, Koestinger M, Bischof H (2012) Relaxed pairwise learned metric for person re-identification. In: European Conference on computer vision (ECCV), pp 780–793
Hu H (2015) Sparse discriminative multimanifold Grassmannian analysis for face recognition with image sets. IEEE Trans Circ Syst Vid Technol 25(10):1599–1611
Jing X-Y, Zhu X, Wu F, Hu R, You X, Wang Y, Feng H, Yang J-Y (2017) Super-resolution person re-identification with semi-coupled low-rank discriminant dictionary learning. IEEE Trans Image Process 26(3):1363–1378
Kim T-K, Arandjelovic O, Cipolla R (2007) Boosted manifold principal angles for image set-based recognition. Pattern Recogn 40(9):2475–2484
Koestinger M, Koestinger M, Wohlhart P, Roth PM, Bischof H (2012) Large scale metric learning from equivalence constraints. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 2288–2295
Kviatkovsky I, Adam A, Rivlin E (2013) Color invariants for person reidentification. IEEE Trans Pattern Anal Mach Intell 35(7):1622–1634
Li W, Zhao R, Xiao T, Wang X (2014) Deepreid: deep filter pairing neural network for person re-identification. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 152–159
Li X, Zheng W-S, Wang X, Xiang T, Gong S (2015) Multi-scale learning for low-resolution person re-identification. In: IEEE Conference on computer vision (ICCV), pp 3765–3773
Liao S, Hu Y, Zhu X, Li SZ (2015) Person re-identification by local maximal occurrence representation and metric learning. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 2197–2206
Lin T, Zha H (2008) Riemannian manifold learning. IEEE Trans Pattern Anal Mach Intell 30(5):796–809
Lisanti G, Masi I, Bagdanov AD, Del Bimbo A (2015) Person re-identification by iterative re-weighted sparse ranking. IEEE Trans Pattern Anal Mach Intell 37 (8):1629–1642
Liu H, Qi M, Jiang J (2015) Kernelized relaxed margin components analysis for person re-identification. IEEE Signal Process Lett 22(7):910–914
Liu K, Ma B, Zhang W, Huang R (2015) A spatio-temporal appearance representation for viceo-based pedestrian re-identification. In: IEEE Conference on computer vision (ICCV), pp 3810–3818
Liu M, Shan S, Wang R, Chen X (2016) Learning expressionlets via universal manifold model for dynamic facial expression recognition. IEEE Trans Image Process 25(12):5920–5932
Lu Y, Wang R, Shan S, Chen X (2016) Multiple-shot person re-identification via Riemannian discriminative learning. In: Asian Conference on computer vision. Springer (ACCV), pp 408–425
McLaughlin N, Martinez del Rincon J, Miller P (2016) Recurrent convolutional network for video-based person re-identification. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 1325–1334
Rao Y, Hou L, Wang Z, Chen L (2014) Illumination-based nighttime video contrast enhancement using genetic algorithm. Multimed Tools Appl 70(3):2235–2254
Sun C, Wang D, Lu H (2017) Person re-identification via distance metric learning with latent variables. IEEE Trans Image Process 26(1):23–34
Tao D, Li X, Wu X, Maybank SJ (2007) General tensor discriminant analysis and Gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell 29(10):1700–1715
Tao D, Guo Y, Song M, Li Y, Yu Z, Tang YY (2016) Person re-identification by dual-regularized kiss metric learning. IEEE Trans Image Process 25 (6):2726–2738
Thompson WB, Shirley P, Ferwerda JA (2002) A spatial post-processing algorithm for images of night scenes. J Graph Tools 7(1):1–12
Tunç B, Gökmen M (2011) Manifold learning for face recognition under changing illumination. Telecommun Syst 47(3–4):185–195
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3 (1):71–86
Wang R, Chen X (2009) Manifold discriminant analysis. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 429–436
Wang X, Doretto G, Sebastian T, Rittscher J, Tu P (2007) Shape and appearance context modeling. In: IEEE Conference on computer vision (ICCV), pp 1–8
Wang R, Shan S, Chen X, Gao W (2008) Manifold-manifold distance with application to face recognition based on image set. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 1–8
Wang T, Gong S, Zhu X, Wang S (2014) Person re-identification by video ranking. In: European Conference on computer vision (ECCV), pp 688–703
Wang W, Wang R, Huang Z, Shan S, Chen X (2015) Discriminant analysis on Riemannian manifold of Gaussian distributions for face recognition with image sets. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 2048–2057
Wang F, Zuo W, Lin L, Zhang D, Zhang L (2016) Joint learning of single-image and cross-image representations for person re-identification. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 1288–1296
Wang Z, Hu R, Yu Y, Jiang J, Liang C, Wang J (2016) Scale-adaptive low-resolution person re-identification via learning a discriminating surface. IJCAI, pp 2669–2675
Wen J, Fowler JE, He M, Zhao Y-Q, Deng C, Menon V (2016) Orthogonal nonnegative matrix factorization combining multiple features for spectral–spatial dimensionality reduction of hyperspectral imagery. IEEE Trans Geosci Remote Sens 54(7):4272–4286
Xiao T, Li H, Ouyang W, Wang X (2016) Learning deep feature representations with domain guided dropout for person re-identification. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 1249–1258
Xiong F, Gou M, Camps O, Sznaier M (2014) Person re-identification using kernel-based metric learning methods. In: European Conference on computer vision (ECCV), pp 1–16
Yan Y, Ni B, Song Z, Ma C, Yan Y, Yang X (2016) Person re-identification via recurrent feature aggregation. In: European Conference on computer vision (ECCV), pp 701–716
Yang Y, Yang J, Yan J, Liao S, Yi D, Li SZ (2014) Salient color names for person re-identification. In: European Conference on computer vision (ECCV), pp 536–551
You J, Wu A, Li X, Zheng W-S (2016) Top-push video-based person re-identification. In: IEEE Conference on computer vision and pattern recognition (ICCV), pp 1345–1353
Zhang R, Lin L, Zhang R, Zuo W, Zhang L (2015) Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Trans Image Process 24(12):4766–4779
Zhao R, Ouyang W, Wang X (2013) Person re-identification by salience matching. In: IEEE Conference on computer vision (ICCV), pp 2528–2535
Zhao R, Ouyang W, Wang X (2013) Unsupervised salience learning for person re-identification. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 3586–3593
Zhao R, Ouyang W, Wang X (2014) Learning mid-level filters for person re-identification. In: IEEE Conference on computer vision and pattern recognition (CVPR), pp 144–151
Zheng W-S, 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, 196–206
Zhu X, Jing X-Y, Wu F, Feng H (2016) Video-based person re-identification by simultaneously learning intra-video and inter-video distance metrics. In: International joint conference on artificial intelligence, pp 3552–3559
Zhu X, Jing X-Y, Wu F, Wang Y, Zuo W, Zheng W-S (2017) Learning heterogeneous dictionary pair with feature projection matrix for pedestrian video retrieval via single query image. In: Association for the advancement of artificial intelligence (AAAI), pp 4341–4348
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Ma, F., Zhu, X., Zhang, X. et al. Low illumination person re-identification. Multimed Tools Appl 78, 337–362 (2019). https://doi.org/10.1007/s11042-018-6239-3
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DOI: https://doi.org/10.1007/s11042-018-6239-3