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Erschienen in: Neural Computing and Applications 10/2020

06.09.2019 | Advances in Parallel and Distributed Computing for Neural Computing

Hierarchical attributes learning for pedestrian re-identification via parallel stochastic gradient descent combined with momentum correction and adaptive learning rate

verfasst von: Keyang Cheng, Fei Tao, Yongzhao Zhan, Maozhen Li, Kenli Li

Erschienen in: Neural Computing and Applications | Ausgabe 10/2020

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Abstract

Convolutional neural networks (CNNs) have obtained high accuracy results for pedestrian re-identification in the past few years. There is always a trade-off between high accuracy and computational time in CNNs. Training CNN is always very difficult as it may take a long time to produce high accuracy results. To overcome this limitation, a novel method parallel stochastic gradient descent (PSGD) is proposed to train a five-hierarchical parallel CNNs that is designed according to pedestrian attributes. Moreover, the momentum correction and adaptive adjustment of learning rate are applied during training process and the time interval for updating parameters is inspected during optimization of parameters selection. The results of this paper prove the effectiveness of proposed PSGD that successfully decreases the training process by five times and surpasses the state-of-the-art methods of pedestrian re-identification in terms of both accuracy and time. The minimum reported running time of the proposed method is 8.7 s which is minimum among all other state-of-the-art methods. These promising results show the efficiency and performance of the proposed model.

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Literatur
1.
Zurück zum Zitat Flores A, Belongie SJ (2010) Removing pedestrians from google street view images. In: Computer vision and pattern recognition, pp 53–58 Flores A, Belongie SJ (2010) Removing pedestrians from google street view images. In: Computer vision and pattern recognition, pp 53–58
2.
Zurück zum Zitat Mwakalonge JL, Siuhi S, White J (2015) Distracted walking: examining the extent to pedestrian safety problems. J Traffic Transp Eng 2(5):327–337 Mwakalonge JL, Siuhi S, White J (2015) Distracted walking: examining the extent to pedestrian safety problems. J Traffic Transp Eng 2(5):327–337
3.
Zurück zum Zitat Zhang J, Wang N, Zhang L (2018) Multi-shot pedestrian re-identification via sequential decision making. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6781–6789 Zhang J, Wang N, Zhang L (2018) Multi-shot pedestrian re-identification via sequential decision making. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6781–6789
4.
Zurück zum Zitat Bo L, Lai K, Ren X, Fox D (2011) Object recognition with hierarchical kernel descriptors. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1729–1736 Bo L, Lai K, Ren X, Fox D (2011) Object recognition with hierarchical kernel descriptors. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1729–1736
5.
Zurück zum Zitat Latifi A, Foglino M, Tanaka K, Williams P, Lazdunski A (1996) A hierarchical quorum-sensing cascade in pseudomonas aeruginosa links the transcriptional activators lasr and rhir (vsmr) to expression of the stationary-phase sigma factor rpos. Mol Microbiol 21(6):1137–1146CrossRef Latifi A, Foglino M, Tanaka K, Williams P, Lazdunski A (1996) A hierarchical quorum-sensing cascade in pseudomonas aeruginosa links the transcriptional activators lasr and rhir (vsmr) to expression of the stationary-phase sigma factor rpos. Mol Microbiol 21(6):1137–1146CrossRef
6.
Zurück zum Zitat Ali H, Hariharan M, Yaacob S, Adom AH, Zaba SK, Elshaikh M (2016) Facial emotion recognition under partial occlusion using empirical mode decomposition. In: Proceedings of the IEEE international symposium on robotics and manufacturing automation, pp 1–6 Ali H, Hariharan M, Yaacob S, Adom AH, Zaba SK, Elshaikh M (2016) Facial emotion recognition under partial occlusion using empirical mode decomposition. In: Proceedings of the IEEE international symposium on robotics and manufacturing automation, pp 1–6
7.
Zurück zum Zitat Yan Z, Zhang H, Piramuthu R, Jagadeesh V (2015) Hd-cnn: Hierarchical deep convolutional neural networks for large scale visual recognition. In: Proceedings of the IEEE international conference on computer vision, pp 2740–2748 Yan Z, Zhang H, Piramuthu R, Jagadeesh V (2015) Hd-cnn: Hierarchical deep convolutional neural networks for large scale visual recognition. In: Proceedings of the IEEE international conference on computer vision, pp 2740–2748
8.
Zurück zum Zitat Oghaz MM, Maarof MA, Rohani MF, Zainal A, Shaid SZ (2019) An optimized skin texture model using gray-level co-occurrence matrix. Neural Comput Appl 31:1835–1853CrossRef Oghaz MM, Maarof MA, Rohani MF, Zainal A, Shaid SZ (2019) An optimized skin texture model using gray-level co-occurrence matrix. Neural Comput Appl 31:1835–1853CrossRef
9.
Zurück zum Zitat Mosca A, Magoulas GD (2019) Customised ensemble methodologies for deep learning: Boosted Residual Networks and related approaches. Neural Comput Appl 31:1713–1731CrossRef Mosca A, Magoulas GD (2019) Customised ensemble methodologies for deep learning: Boosted Residual Networks and related approaches. Neural Comput Appl 31:1713–1731CrossRef
10.
Zurück zum Zitat Guo J, Gould S (2016) Depth dropout: efficient training of residual convolutional neural networks. In: Proceedings of the international conference on digital image computing: techniques and applications, pp 1–7 Guo J, Gould S (2016) Depth dropout: efficient training of residual convolutional neural networks. In: Proceedings of the international conference on digital image computing: techniques and applications, pp 1–7
11.
Zurück zum Zitat Cheng K, Xu F, Tao F, Qi M, Li M (2017) Data-driven pedestrian re-identification based on hierarchical semantic representation. Concurr Comput Pract Exp 9:e4403 Cheng K, Xu F, Tao F, Qi M, Li M (2017) Data-driven pedestrian re-identification based on hierarchical semantic representation. Concurr Comput Pract Exp 9:e4403
12.
Zurück zum Zitat Bhinge S, Levin-Schwartz Y, Adal T (2017) Data-driven fusion of multi-camera video sequences: application to abandoned object detection. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, pp 1697–1701 Bhinge S, Levin-Schwartz Y, Adal T (2017) Data-driven fusion of multi-camera video sequences: application to abandoned object detection. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, pp 1697–1701
13.
Zurück zum Zitat Su C, Zhang S, Xing J, Gao W, Tian Q (2016) Deep attributes driven multi-camera person re-identification. In: Proceedings of the European conference on computer vision, pp 475–491 Su C, Zhang S, Xing J, Gao W, Tian Q (2016) Deep attributes driven multi-camera person re-identification. In: Proceedings of the European conference on computer vision, pp 475–491
14.
Zurück zum Zitat Danaci EG, Ikizlercinbis N (2016) Low-level features for visual attribute recognition. Pattern Recognit Lett 84:185–191CrossRef Danaci EG, Ikizlercinbis N (2016) Low-level features for visual attribute recognition. Pattern Recognit Lett 84:185–191CrossRef
15.
Zurück zum Zitat Gao M, Ai H, Bai B (2016) A feature fusion strategy for person re-identification In: Proceedings of the international conference on image processing, pp 4274–4278 Gao M, Ai H, Bai B (2016) A feature fusion strategy for person re-identification In: Proceedings of the international conference on image processing, pp 4274–4278
16.
Zurück zum Zitat Cheng K, Hui K, Zhan Y (2017) Sparse representations based distributed attribute learning for person re-identification In: Multimedia tools and applications. Springer, New York, pp 25015–25037 Cheng K, Hui K, Zhan Y (2017) Sparse representations based distributed attribute learning for person re-identification In: Multimedia tools and applications. Springer, New York, pp 25015–25037
17.
Zurück zum Zitat Cheng K, Tan X, Li M (2014) Sparse representations based attribute learning for flower classification. In: Neurocomputing. Elsevier, pp 416–426 Cheng K, Tan X, Li M (2014) Sparse representations based attribute learning for flower classification. In: Neurocomputing. Elsevier, pp 416–426
18.
Zurück zum Zitat Dass J, Sharma M, Hassan E, Ghosh H (2013) A density based method for automatic hairstyle discovery and recognition. In: Proceedings of the national conference on computer vision, pattern recognition, image processing and graphics, pp 1–4 Dass J, Sharma M, Hassan E, Ghosh H (2013) A density based method for automatic hairstyle discovery and recognition. In: Proceedings of the national conference on computer vision, pattern recognition, image processing and graphics, pp 1–4
19.
Zurück zum Zitat Kang S, Lee D, Yoo CD (2015) Face attribute classification using attribute-aware correlation map and gated convolutional neural networks. In: Proceedings of the international conference on image processing, pp 4922–4926 Kang S, Lee D, Yoo CD (2015) Face attribute classification using attribute-aware correlation map and gated convolutional neural networks. In: Proceedings of the international conference on image processing, pp 4922–4926
20.
Zurück zum Zitat Lazo-Cortes MS, Carrasco-Ochoa JA, Sanchez-Diaz G (2013) Easy categorization of attributes in decision tables based on basic binary discernibility matrix. In: Iberoamerican congress on pattern recognition. Springer, New York, pp 302–310 Lazo-Cortes MS, Carrasco-Ochoa JA, Sanchez-Diaz G (2013) Easy categorization of attributes in decision tables based on basic binary discernibility matrix. In: Iberoamerican congress on pattern recognition. Springer, New York, pp 302–310
21.
Zurück zum Zitat Nguyen TP, Manzanera A, Kropatsch WG (2014) Impact of topology-related attributes from local binary patterns on texture classification. In: Proceedings of the European conference on computer vision, pp 80–93 Nguyen TP, Manzanera A, Kropatsch WG (2014) Impact of topology-related attributes from local binary patterns on texture classification. In: Proceedings of the European conference on computer vision, pp 80–93
22.
Zurück zum Zitat Liu Y, Yang J, Huang Y, Xu L, Li S, Qi M (2015) Mapreduce based parallel neural networks in enabling large scale machine learning. Comput Intell Neurosci 2015:297672–297672 Liu Y, Yang J, Huang Y, Xu L, Li S, Qi M (2015) Mapreduce based parallel neural networks in enabling large scale machine learning. Comput Intell Neurosci 2015:297672–297672
23.
Zurück zum Zitat Vedaldi A, Lenc K (2014) Matconvnet: convolutional neural networks for matlab. In: Proceedings of the 23rd ACM international conference on multimedia, pp 689–692 Vedaldi A, Lenc K (2014) Matconvnet: convolutional neural networks for matlab. In: Proceedings of the 23rd ACM international conference on multimedia, pp 689–692
24.
Zurück zum Zitat Xiao G, Li K, Li K, Xu Z (2015) Efficient top-(k, l) top range query processing for uncertain data based on multicore architectures. Distrib Parallel Databases 33(3):381–413CrossRef Xiao G, Li K, Li K, Xu Z (2015) Efficient top-(k, l) top range query processing for uncertain data based on multicore architectures. Distrib Parallel Databases 33(3):381–413CrossRef
25.
Zurück zum Zitat Rafegas I, Vanrell M (2017) Color representation in cnns: parallelisms with biological vision. In: Proceedings of the IEEE international conference on computer vision workshop, pp 2697–2705 Rafegas I, Vanrell M (2017) Color representation in cnns: parallelisms with biological vision. In: Proceedings of the IEEE international conference on computer vision workshop, pp 2697–2705
26.
Zurück zum Zitat Song L, Wang Y, Han Y, Zhao X, Liu B, Li X (2016) C-brain: a deep learning accelerator that tames the diversity of cnns through adaptive data-level parallelization. In: Proceedings of the design automation conference, p 123 Song L, Wang Y, Han Y, Zhao X, Liu B, Li X (2016) C-brain: a deep learning accelerator that tames the diversity of cnns through adaptive data-level parallelization. In: Proceedings of the design automation conference, p 123
27.
Zurück zum Zitat Chen J, Li K, Bilal K, Zhou X, Li K, Yu PS (2019) A bi-layered parallel training architecture for large-scale convolutional neural networks. In: IEEE, transactions on parallel and distributed systems, pp 965–976 Chen J, Li K, Bilal K, Zhou X, Li K, Yu PS (2019) A bi-layered parallel training architecture for large-scale convolutional neural networks. In: IEEE, transactions on parallel and distributed systems, pp 965–976
28.
Zurück zum Zitat Li K, Tang X, Veeravalli B, Li K (2015) Scheduling precedence constrained stochastic tasks on heterogeneous cluster systems. IEEE Trans Comput 64(1):191–204MathSciNetMATHCrossRef Li K, Tang X, Veeravalli B, Li K (2015) Scheduling precedence constrained stochastic tasks on heterogeneous cluster systems. IEEE Trans Comput 64(1):191–204MathSciNetMATHCrossRef
29.
Zurück zum Zitat Li K, Yang W, Li K (2015) Performance analysis and optimization for SpMV on GPU using probabilistic modeling. IEEE Trans Parallel Distrib Syst 26(1):196–205MathSciNetCrossRef Li K, Yang W, Li K (2015) Performance analysis and optimization for SpMV on GPU using probabilistic modeling. IEEE Trans Parallel Distrib Syst 26(1):196–205MathSciNetCrossRef
30.
Zurück zum Zitat Chen J, Li K, Deng Q, Li K (2019) Distributed deep learning model for intelligent video surveillance systems with edge computing. In: IEEE, transactions on industrial informatics, p 1 Chen J, Li K, Deng Q, Li K (2019) Distributed deep learning model for intelligent video surveillance systems with edge computing. In: IEEE, transactions on industrial informatics, p 1
31.
Zurück zum Zitat Huanzhou Z, Zhuoer G, Haiming Z, Keyang C, Chang-Tsun L, Ligang H (2018) Developing a pattern discovery method in time series data and its GPU acceleration. In: TUP, Big data mining and analytics, pp 266–283 Huanzhou Z, Zhuoer G, Haiming Z, Keyang C, Chang-Tsun L, Ligang H (2018) Developing a pattern discovery method in time series data and its GPU acceleration. In: TUP, Big data mining and analytics, pp 266–283
32.
Zurück zum Zitat Loshchilov I, Hutter F (2016) Sgdr: stochastic gradient descent with restarts. In: Proceedings of the international conference on learning representations Loshchilov I, Hutter F (2016) Sgdr: stochastic gradient descent with restarts. In: Proceedings of the international conference on learning representations
33.
Zurück zum Zitat Wang L, Yang Y, Min MR, Chakradhar ST (2017) Accelerating deep neural network training with inconsistent stochastic gradient descent. In: Neural networks the official journal of the international neural network society. Elsevier, pp 219–229 Wang L, Yang Y, Min MR, Chakradhar ST (2017) Accelerating deep neural network training with inconsistent stochastic gradient descent. In: Neural networks the official journal of the international neural network society. Elsevier, pp 219–229
34.
Zurück zum Zitat Sutskever I, Martens J, Dahl GE, Hinton GE (2013) On the importance of initialization and momentum in deep learning. In: Proceedings of the international conference on machine learning, pp 1139–1147 Sutskever I, Martens J, Dahl GE, Hinton GE (2013) On the importance of initialization and momentum in deep learning. In: Proceedings of the international conference on machine learning, pp 1139–1147
35.
Zurück zum Zitat Fan Q, Wu W, Zurada JM (2016) Convergence of batch gradient learning with smoothing regularization and adaptive momentum for neural networks. SpringerPlus 5(1):295CrossRef Fan Q, Wu W, Zurada JM (2016) Convergence of batch gradient learning with smoothing regularization and adaptive momentum for neural networks. SpringerPlus 5(1):295CrossRef
36.
Zurück zum Zitat Botev A, Lever G, Barber D (2016) Nesterov’s accelerated gradient and momentum as approximations to regularised update descent In: Proceedings of the international joint conference on neural network, pp 1899–1903 Botev A, Lever G, Barber D (2016) Nesterov’s accelerated gradient and momentum as approximations to regularised update descent In: Proceedings of the international joint conference on neural network, pp 1899–1903
37.
Zurück zum Zitat Hadgu AT, Nigam A, Diaz-Aviles E (2015) Large-scale learning with adagrad on spark. In: Proceedings of the IEEE international conference on Big Data, pp 2828–2830 Hadgu AT, Nigam A, Diaz-Aviles E (2015) Large-scale learning with adagrad on spark. In: Proceedings of the IEEE international conference on Big Data, pp 2828–2830
38.
Zurück zum Zitat Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: Proceedings of the international conference on learning representations Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: Proceedings of the international conference on learning representations
39.
Zurück zum Zitat Li Y, Tong G, Li X, Wang Y, Zou B, Liu Y (2019) PARNet: a joint loss function and dynamic weights network for pedestrian semantic attributes recognition of smart surveillance image. In: Multidisciplinary digital publishing institute, applied sciences, p 2027 Li Y, Tong G, Li X, Wang Y, Zou B, Liu Y (2019) PARNet: a joint loss function and dynamic weights network for pedestrian semantic attributes recognition of smart surveillance image. In: Multidisciplinary digital publishing institute, applied sciences, p 2027
40.
Zurück zum Zitat Hajj Nadine, Awad Mariette (2019) A piecewise weight update rule for a supervised training of cortical algorithms. Neural Comput Appl 31:1915–1930CrossRef Hajj Nadine, Awad Mariette (2019) A piecewise weight update rule for a supervised training of cortical algorithms. Neural Comput Appl 31:1915–1930CrossRef
41.
Zurück zum Zitat Chatzipavlis A, Tsekouras GE, Trygonis V, Velegrakis AF, Tsimikas J, Rigos A, Salmas C (2019) Modeling beach realignment using a neuro-fuzzy network optimized by a novel backtracking search algorithm. Neural Comput Appl 31:1747–1763CrossRef Chatzipavlis A, Tsekouras GE, Trygonis V, Velegrakis AF, Tsimikas J, Rigos A, Salmas C (2019) Modeling beach realignment using a neuro-fuzzy network optimized by a novel backtracking search algorithm. Neural Comput Appl 31:1747–1763CrossRef
42.
Zurück zum Zitat Chen Y, Duffner S, Stoian A, Dufour J, Baskurt A (2018) Pedestrian attribute recognition with part-based CNN and combined feature representations. In: Proceedings of the international joint conference on computer vision imaging and computer graphics theory and applications, pp 114–122 Chen Y, Duffner S, Stoian A, Dufour J, Baskurt A (2018) Pedestrian attribute recognition with part-based CNN and combined feature representations. In: Proceedings of the international joint conference on computer vision imaging and computer graphics theory and applications, pp 114–122
43.
Zurück zum Zitat Li D, Chen X, Zhang Z, Huang K (2018) Pose guided deep model for pedestrian attribute recognition in surveillance scenarios. In: Proceedings of the IEEE international conference on multimedia and expo (ICME), pp 1–6 Li D, Chen X, Zhang Z, Huang K (2018) Pose guided deep model for pedestrian attribute recognition in surveillance scenarios. In: Proceedings of the IEEE international conference on multimedia and expo (ICME), pp 1–6
44.
Zurück zum Zitat Chen Z, Li A, Wang Y (2019) Video-Based Pedestrian Attribute Recognition In: Computer vision and pattern recognition. arXiv:1901.05742 Chen Z, Li A, Wang Y (2019) Video-Based Pedestrian Attribute Recognition In: Computer vision and pattern recognition. arXiv:​1901.​05742
45.
Zurück zum Zitat Cai L, Zhu J, Zeng H, Chen J, Cai C, Ma K (2018) Hog-assisted deep feature learning for pedestrian gender recognition. J Frank Inst 355:1991–2008CrossRef Cai L, Zhu J, Zeng H, Chen J, Cai C, Ma K (2018) Hog-assisted deep feature learning for pedestrian gender recognition. J Frank Inst 355:1991–2008CrossRef
46.
Zurück zum Zitat Wang X, Zheng S, Yang R, Luo B, Tang J (2019) Pedestrian attribute recognition: a survey. In: Computer vision and pattern recognition. arXiv:1901.07474 Wang X, Zheng S, Yang R, Luo B, Tang J (2019) Pedestrian attribute recognition: a survey. In: Computer vision and pattern recognition. arXiv:​1901.​07474
47.
Zurück zum Zitat Li D, Zhang Z, Chen X, Ling H, Huang K (2016) A richly annotated dataset for pedestrian attribute recognition. In: Computer vision and pattern recognition. arXiv:1603.07054 Li D, Zhang Z, Chen X, Ling H, Huang K (2016) A richly annotated dataset for pedestrian attribute recognition. In: Computer vision and pattern recognition. arXiv:​1603.​07054
48.
Zurück zum Zitat Bottou Leon (2012) Stochastic gradient descent tricks. In: Neural networks: tricks of the trade. Springer, New York, pp 421–436 Bottou Leon (2012) Stochastic gradient descent tricks. In: Neural networks: tricks of the trade. Springer, New York, pp 421–436
49.
Zurück zum Zitat Dong X, Tsong Y, Shen M (2014) Equivalence tests for interchangeability based on two one-sided probabilities. J Biopharm Stat 24(6):1332–1348MathSciNetCrossRef Dong X, Tsong Y, Shen M (2014) Equivalence tests for interchangeability based on two one-sided probabilities. J Biopharm Stat 24(6):1332–1348MathSciNetCrossRef
50.
Zurück zum Zitat Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: Proceedings of the IEEE international workshop on performance evaluation for tracking and surveillance, vol 3(5), pp 501–512 Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: Proceedings of the IEEE international workshop on performance evaluation for tracking and surveillance, vol 3(5), pp 501–512
51.
Zurück zum Zitat Li W, Wang X (2013) Locally aligned feature transforms across views. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3594–3601 Li W, Wang X (2013) Locally aligned feature transforms across views. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3594–3601
52.
Zurück zum Zitat Li W, Zhao R, Wang X (2012) Human reidentification with transferred metric learning. In: Asian conference on computer vision. Springer, New York, pp 31–44 Li W, Zhao R, Wang X (2012) Human reidentification with transferred metric learning. In: Asian conference on computer vision. Springer, New York, pp 31–44
53.
Zurück zum Zitat Ristani E, Solera F, Zou R, Cucchiara R, Tomasi C (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: Proceedings of the European conference on computer vision workshops, pp 17–35 Ristani E, Solera F, Zou R, Cucchiara R, Tomasi C (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: Proceedings of the European conference on computer vision workshops, pp 17–35
54.
Zurück zum Zitat Hoang VD, Le MH, Jo KH (2014) Hybrid cascade boosting machine using variant scale blocks based hog features for pedestrian detection. Neurocomputing 135(C):357–366CrossRef Hoang VD, Le MH, Jo KH (2014) Hybrid cascade boosting machine using variant scale blocks based hog features for pedestrian detection. Neurocomputing 135(C):357–366CrossRef
55.
Zurück zum Zitat Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of the international conference on learning representations Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of the international conference on learning representations
56.
Zurück zum Zitat Jung H, Choi MK, Jung J, Lee JH, Kwon S, Jung WY (2017) Resnet-based vehicle classification and localization in traffic surveillance systems. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 934–940 Jung H, Choi MK, Jung J, Lee JH, Kwon S, Jung WY (2017) Resnet-based vehicle classification and localization in traffic surveillance systems. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 934–940
57.
Zurück zum Zitat Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Rabinovich A (2015) Going deeper with convolutions In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9 Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Rabinovich A (2015) Going deeper with convolutions In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9
58.
Zurück zum Zitat Chollet François (2017) Xception: Deep learning with depthwise separable convolutions In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258 Chollet François (2017) Xception: Deep learning with depthwise separable convolutions In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258
59.
Zurück zum Zitat Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(< 0.5\) MB model size. In: Computer vision and pattern recognition. arXiv:1602.07360 Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(< 0.5\) MB model size. In: Computer vision and pattern recognition. arXiv:​1602.​07360
60.
Zurück zum Zitat Layne R, Hospedales TM, Gong S (2014) Attributes-based re-identification. Springer, London (Person Re-Identification) Layne R, Hospedales TM, Gong S (2014) Attributes-based re-identification. Springer, London (Person Re-Identification)
61.
Zurück zum Zitat Roth PM, Hirzer M, Kostinger M, Beleznai C, Bischof H (2014) Mahalanobis distance learning for person re-identification. In: Springer, London (Person Re-Identification), pp 247–267 Roth PM, Hirzer M, Kostinger M, Beleznai C, Bischof H (2014) Mahalanobis distance learning for person re-identification. In: Springer, London (Person Re-Identification), pp 247–267
62.
Zurück zum Zitat Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: Computer vision and pattern recognition, pp 2360–2367 Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: Computer vision and pattern recognition, pp 2360–2367
63.
Zurück zum Zitat Layne R, Hospedales TM, Gong S (2012) Person re-identification by attributes. In: British machine vision conference, pp 1–11 Layne R, Hospedales TM, Gong S (2012) Person re-identification by attributes. In: British machine vision conference, pp 1–11
64.
Zurück zum Zitat Umeda T, Sun Y, Irie G, Sudo K, Kinebuchi T (2016) Attribute discovery for person re-identification. In: International conference on multimedia modeling. Springer, New York, pp 268–276 Umeda T, Sun Y, Irie G, Sudo K, Kinebuchi T (2016) Attribute discovery for person re-identification. In: International conference on multimedia modeling. Springer, New York, pp 268–276
65.
Zurück zum Zitat Zhao R, Ouyang W, Wang X (2013) Unsupervised salience learning for person re-identification. In: Proceedings of the ieee conference on computer vision and pattern recognition, pp 3586–3593 Zhao R, Ouyang W, Wang X (2013) Unsupervised salience learning for person re-identification. In: Proceedings of the ieee conference on computer vision and pattern recognition, pp 3586–3593
Metadaten
Titel
Hierarchical attributes learning for pedestrian re-identification via parallel stochastic gradient descent combined with momentum correction and adaptive learning rate
verfasst von
Keyang Cheng
Fei Tao
Yongzhao Zhan
Maozhen Li
Kenli Li
Publikationsdatum
06.09.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 10/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04485-2

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