Skip to main content
SpringerLink
Log in

A half-precision compressive sensing framework for end-to-end person re-identification

  • Review Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Compressive sensing (CS) approaches are useful for end-to-end person re-identification (Re-ID) in reducing the overheads of transmitting and storing video frames in distributed multi-camera systems. However, the reconstruction quality degrades appreciably as the measurement rate decreases for existing CS methods. To address this problem, we propose a half-precision CS framework for end-to-end person Re-ID named HCS4ReID, which efficiently recoveries detailed features of the person-of-interest regions in video frames. HCS4ReID supports half-precision CS sampling, transmitting and storing CS measurements with half-precision floats, and CS reconstruction with two measurement rates. Extensive experiments implemented on the PRW dataset indicate that the proposed HCS4ReID achieves 1.55 \(\times\) speedups over the single-precision counterpart on average for the CS sampling on an Intel HD Graphics 530, and only half-network bandwidth and storage space are needed to transmit and store the generated CS measurements. Comprehensive evaluations demonstrate that the proposed HCS4ReID is a scalable and portable CS framework with two measurement rates, and suitable for end-to-end person Re-ID. Especially, it achieves the comparable performance on the reconstructed PRW dataset against CS reconstruction with single-precision floats and a single measurement rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Ahmed E, Jones M, Marks TK (2015) An improved deep learning architecture for person re-identification. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 3908–3916. https://doi.org/10.1109/CVPR.2015.7299016

  2. Bashir K, Xiang T, Gong S (2008) Feature selection on gait energy image for human identification. In: 2008 IEEE international conference on acoustics, speech and signal processing, pp 985–988. https://doi.org/10.1109/ICASSP.2008.4517777

  3. Chen C, Li K, Teo SG, Chen G, Zou X, Yang X, Vijay RC, Feng J, Zeng Z (2018) Exploiting spatio-temporal correlations with multiple 3d convolutional neural networks for citywide vehicle flow prediction. In: 2018 IEEE international conference on data mining (ICDM), pp 893–898. https://doi.org/10.1109/ICDM.2018.00107

  4. Chen J, Fang J, Liu W, Tang T, Yang C (2018) CLMF: a fine-grained and portable alternating least squares algorithm for parallel matrix factorization. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2018.04.071

    Article  Google Scholar 

  5. Chen J, Li K, Tang Z, Bilal K, Yu S, Weng C, Li K (2017) A parallel random forest algorithm for big data in a spark cloud computing environment. IEEE Trans Parallel Distrib Syst 28(4):919–933. https://doi.org/10.1109/TPDS.2016.2603511

    Article  Google Scholar 

  6. Chen S, Guo C, Lai J (2016) Deep ranking for person re-identification via joint representation learning. IEEE Trans Image Process 25(5):2353–2367. https://doi.org/10.1109/TIP.2016.2545929

    Article  MathSciNet  MATH  Google Scholar 

  7. Chen Y, Duffner S, Baskurt A, Stoian A, Dufour JY (2018) Similarity learning with listwise ranking for person re-identification. In: 2018 25th IEEE international conference on image processing (ICIP), pp 843–847. https://doi.org/10.1109/ICIP.2018.8451628

  8. 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: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1335–1344. https://doi.org/10.1109/CVPR.2016.149

  9. Courbariaux M, Bengio Y, David JP (2015) Training deep neural networks with low precision multiplications. ArXiv preprint arXiv:1412.7024v5

  10. 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. https://doi.org/10.1016/j.patcog.2015.04.005

    Article  Google Scholar 

  11. Dinh KQ, Jeon B (2017) Iterative weighted recovery for block-based compressive sensing of image/video at a low subrate. IEEE Trans Circuits Syst Video Technol 27(11):2294–2308. https://doi.org/10.1109/TCSVT.2016.2587398

    Article  Google Scholar 

  12. Dollár P, Appel R, Belongie S, Perona P (2014) Fast feature pyramids for object detection. IEEE Trans Pattern Anal Mach Intell 36(8):1532–1545. https://doi.org/10.1109/TPAMI.2014.2300479

    Article  Google Scholar 

  13. Duan M, Li K, Li K (2018) An ensemble cnn2elm for age estimation. IEEE Trans Inf Forensics Secur 13(3):758–772. https://doi.org/10.1109/TIFS.2017.2766583

    Article  Google Scholar 

  14. Duarte MF, Davenport MA, Takhar D, Laska JN, Sun T, Kelly KF, Baraniuk RG (2008) Single-pixel imaging via compressive sampling. IEEE Signal Process Mag 25(2):83–91. https://doi.org/10.1109/MSP.2007.914730

    Article  Google Scholar 

  15. Fang J, Varbanescu AL, Liao X, Sips H (2014) Evaluating vector data type usage in opencl kernels. Concurr Comput Pract Exp 27(17):4586–4602. https://doi.org/10.1002/cpe.3424

    Article  Google Scholar 

  16. Fang J, Zhang P, Tang C, Huang T, Yang C (2017) Implementing and evaluating OpenCL on an ARMv8 multi-core CPU. In: IEEE international symposium on parallel and distributed processing with applications. IEEE Computer Society, Guangzhou, Guangdong, China, pp 860–867. https://doi.org/10.1109/ISPA/IUCC.2017.00131

  17. 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. https://doi.org/10.1109/TPAMI.2009.167

    Article  Google Scholar 

  18. Ge Y, Gu X, Chen M, Wang H, Yang D (2018) Deep multi-metric learning for person re-identification. In: 2018 IEEE international conference on multimedia and expo (ICME), pp 1–6. https://doi.org/10.1109/ICME.2018.8486502

  19. Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Forsyth D, Torr P, Zisserman A (eds) Computer Vision: ECCV 2008. Springer, Berlin, pp 262–275

    Chapter  Google Scholar 

  20. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  21. Iliadis M, Spinoulas L, Katsaggelos AK (2018) Deep fully-connected networks for video compressive sensing. Dig Signal Process 72:9–18. https://doi.org/10.1016/j.dsp.2017.09.010

    Article  Google Scholar 

  22. Joseph R, Ali F (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767

  23. Kulkarni K, Lohit S, Turaga P, Kerviche R, Ashok A (2016) Reconnet: non-iterative reconstruction of images from compressively sensed measurements. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 449–458. https://doi.org/10.1109/CVPR.2016.55

  24. Li J, Liang X, Shen S, Xu T, Feng J, Yan S (2018) Scale-aware fast R-CNN for pedestrian detection. IEEE Trans Multimed 20(4):985–996. https://doi.org/10.1109/TMM.2017.2759508

    Article  Google Scholar 

  25. Li K, Tang X, Li K (2014) Energy-efficient stochastic task scheduling on heterogeneous computing systems. IEEE Trans Parallel Distrib Syst 25(11):2867–2876. https://doi.org/10.1109/TPDS.2013.270

    Article  Google Scholar 

  26. Li K, Tang X, Veeravalli B, Li K (2015) Scheduling precedence constrained stochastic tasks on heterogeneous cluster systems. IEEE Trans Comput 64(1):191–204. https://doi.org/10.1109/TC.2013.205

    Article  MathSciNet  MATH  Google Scholar 

  27. Li W, Zhao R, Xiao T, Wang X (2014) Deepreid: deep filter pairing neural network for person re-identification. In: 2014 IEEE conference on computer vision and pattern recognition, pp 152–159. https://doi.org/10.1109/CVPR.2014.27

  28. Liao L, Li K, Li K, Yang C, Tian Q (2018) UHCL-Darknet: an OpenCL-based deep neural network framework for heterogeneous multi-/many-core clusters. In: Proceedings of the 47th international conference on parallel processing, ICPP 2018. ACM, New York, NY, USA, pp 44:1–44:10. https://doi.org/10.1145/3225058.3225107

  29. Metzler CA, Maleki A, Baraniuk RG (2016) From denoising to compressed sensing. IEEE Trans Inf Theory 62(9):5117–5144. https://doi.org/10.1109/TIT.2016.2556683

    Article  MathSciNet  MATH  Google Scholar 

  30. Micikevicius P, Narang S, Alben J, Diamos GF, Elsen E, Garca D, Ginsburg B, Houston M, Kuchaiev O, Venkatesh G, Wu H (2018) Mixed precision training. In: The 6th international conference on learning representations (ICLR 2018), pp 1–12

  31. Mousavi A, Baraniuk RG (2017) Learning to invert: signal recovery via deep convolutional networks. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2272–2276. https://doi.org/10.1109/ICASSP.2017.7952561

  32. Mousavi A, Patel AB, Baraniuk RG (2015) A deep learning approach to structured signal recovery. In: 2015 53rd annual allerton conference on communication, control, and computing (Allerton), pp 1336–1343. https://doi.org/10.1109/ALLERTON.2015.7447163

  33. Nugteren C (2018) Clblast: a tuned OpenCL BLAS library. In: Proceedings of the international workshop on OpenCL, IWOCL ’18. ACM, New York, NY, USA, pp 5:1–5:10. https://doi.org/10.1145/3204919.3204924

  34. Ouyang W, Wang X (2013) Joint deep learning for pedestrian detection. In: 2013 IEEE international conference on computer vision, pp 2056–2063. https://doi.org/10.1109/ICCV.2013.257

  35. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  36. Shi L, Chen H, Sun J, Li K (2012) vCUDA: GPU-accelerated high-performance computing in virtual machines. IEEE Trans Comput 61(6):804–816. https://doi.org/10.1109/TC.2011.112

    Article  MathSciNet  MATH  Google Scholar 

  37. Shi W, Jiang F, Zhang S, Zhao D (2017) Deep networks for compressed image sensing. In: 2017 IEEE international conference on multimedia and expo (ICME). IEEE, pp 877–882

  38. Sun Y, Zheng L, Yang Y, Tian Q, Wang S (2018) Beyond part models: person retrieval with refined part pooling. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) European conference on computer vision (ECCV). Springer, Cham, pp 501–518

  39. Tao D, Guo Y, Yu B, Pang J, Yu Z (2018) Deep multi-view feature learning for person re-identification. IEEE Trans Circuits Syst Video Technol 28(10):2657–2666. https://doi.org/10.1109/TCSVT.2017.2726580

    Article  Google Scholar 

  40. Vezzani R, Baltieri D, Cucchiara R (2013) People reidentification in surveillance and forensics: a survey. ACM Comput Surv 46(2):29:1–29:37. https://doi.org/10.1145/2543581.2543596

    Article  Google Scholar 

  41. Wang G, Yuan Y, Chen X, Li J, Zhou X (2018) Learning discriminative features with multiple granularities for person re-identification. In: Proceedings of the 26th ACM international conference on multimedia, MM ’18. ACM, New York, NY, USA, pp 274–282. https://doi.org/10.1145/3240508.3240552

  42. Wojek C, Dollar P, Schiele B, Perona P (2012) Pedestrian detection: an evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34:743–761. https://doi.org/10.1109/TPAMI.2011.155

    Article  Google Scholar 

  43. Xiao T, Li H, Ouyang W, Wang X (2016) Learning deep feature representations with domain guided dropout for person re-identification. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR) pp 1249–1258. https://doi.org/10.1109/CVPR.2016.140

  44. Xiao T, Li S, Wang B, Lin L, Wang X (2017) Joint detection and identification feature learning for person search. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 3376–3385. https://doi.org/10.1109/CVPR.2017.360

  45. Xu Y, Li K, He L, Zhang L, Li K (2015) A hybrid chemical reaction optimization scheme for task scheduling on heterogeneous computing systems. IEEE Trans Parallel Distrib Syst 26(12):3208–3222. https://doi.org/10.1109/TPDS.2014.2385698

    Article  Google Scholar 

  46. Zhang H, Cao X, Ho JKL, Chow TWS (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inform 13(2):520–531. https://doi.org/10.1109/TII.2016.2605629

    Article  Google Scholar 

  47. Zhang H, Ji Y, Huang W, Liu L (2018) Sitcom-star-based clothing retrieval for video advertising: a deep learning framework. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3579-x

    Article  Google Scholar 

  48. Zhang J, Ghanem B (2018) ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1828–1837

  49. Zhang L, Li K, Xu Y, Mei J, Zhang F, Li K (2015) Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster. Inf Sci 319:113–131. https://doi.org/10.1016/j.ins.2015.02.023

    Article  MathSciNet  Google Scholar 

  50. Zhang L, Lin L, Liang X, He K (2016) Is faster R-CNN doing well for pedestrian detection? In: Leibe B, Matas J, Sebe N, Welling M (eds) ECCV 2016. Springe, Cham, pp 443–457

    Google Scholar 

  51. Zhang N, Paluri M, Taigman Y, Fergus R, Bourdev L (2015) Beyond frontal faces: improving person recognition using multiple cues. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 4804–4813. https://doi.org/10.1109/CVPR.2015.7299113

  52. Zhang P, Fang J, Tang T, Yang C, Wang Z (2018) Mocl: an efficient OpenCL implementation for the matrix-2000 architecture. In: ACM international conference on computing frontiers. ACM, Ischia, Italy. https://doi.org/10.1145/3203217.3203244

  53. Zhang P, Fang J, Tang T, Yang C, Wang Z (2018) Tuning streamed applications on Intel Xeon Phi: a machine learning based approach. In: the 32nd IEEE international parallel and distributed processing symposium (IPDPS’18). Vancouver, British Columbia, Canada, pp 515–525

  54. Zhang S, Benenson R, Omran M, Hosang J, Schiele B (2016) How far are we from solving pedestrian detection? In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1259–1267. https://doi.org/10.1109/CVPR.2016.141

  55. Zhao L, Li X, Zhuang Y, Wang J (2017) Deeply-learned part-aligned representations for person re-identification. In: 2017 IEEE international conference on computer vision (ICCV), pp 3239–3248. https://doi.org/10.1109/ICCV.2017.349

  56. Zheng L, Bie Z, Sun Y, Wang J, Su C, Wang S, Tian Q (2016) Mars: a video benchmark for large-scale person re-identification. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision: ECCV 2016. Springer, Cham, pp 868–884

    Chapter  Google Scholar 

  57. Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: a benchmark. In: 2015 IEEE international conference on computer vision (ICCV), pp 1116–1124. https://doi.org/10.1109/ICCV.2015.133

  58. Zheng L, Zhang H, Sun S, Chandraker M, Yang Y, Tian Q (2017) Person re-identification in the wild. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 3346–3355. https://doi.org/10.1109/CVPR.2017.357

  59. Zheng W, Gong S, Xiang T (2011) Person re-identification by probabilistic relative distance comparison. In: CVPR 2011, pp 649–656. https://doi.org/10.1109/CVPR.2011.5995598

Download references

Acknowledgements

The research was partially funded by the Program of National Natural Science Foundation of China (Grant No. 61751204), the National Outstanding Youth Science Program of National Natural Science Foundation of China (Grant No. 61625202), the International (Regional) Cooperation and Exchange Program of National Natural Science Foundation of China (Grant No. 61661146006), the National Key R&D Program of China (Grant Nos. 2016YFB0201303, 2016YFB0200201), the National Natural Science Foundation of China (Grant Nos. 61772182, 61802032), Science and Technology Plan of Changsha (K1705032). The authors would like to thank Tianming Jin for his help in improving the paper.

Author information

Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410073, China

    Longlong Liao & Jie Liu

  2. State Key Laboratory of High Performance Computing, Changsha, 410073, China

    Longlong Liao

  3. College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, China

    Zhibang Yang

  4. Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, China

    Qing Liao

  5. College of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Kenli Li

  6. Department of Computer Science, State University of New York, New Paltz, NY, 12561, USA

    Keqin Li

  7. Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, 78249, USA

    Qi Tian

Authors
  1. Longlong Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhibang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qing Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kenli Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Keqin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jie Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qi Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Longlong Liao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liao, L., Yang, Z., Liao, Q. et al. A half-precision compressive sensing framework for end-to-end person re-identification. Neural Comput & Applic 32, 1141–1155 (2020). https://doi.org/10.1007/s00521-019-04424-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04424-1

Keywords

Search

Navigation