3.5層人工智慧邊緣運算物聯網閘道器及其在步態辨識和行人重識別的應用

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姓名

劉肇資(Chao-tsu) 查詢紙本館藏

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資訊工程學系

論文名稱

3.5層人工智慧邊緣運算物聯網閘道器及其在步態辨識和行人重識別的應用
(3.5-Tier AIoT Edge Computing Gateway and Its Applications on Gait Recognition and Person Re-Identification)

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至系統瀏覽論文 (2027-1-7以後開放)

摘要(中)

隨著愈來愈多物聯網設備產生的影像資料和進階的影像辨識應用，各種軟體硬體架構不敷所需，還有隨之而來的隱私需求，傳統工業物聯網閘道器所提供的運算資源和架構已經無法符合需求。在跨攝影機的生物識別技術上，需要消耗大量的AI（Artificial Intelligence）運算資源，也有著針對不同應用和規模調整閘道器大小的需求，並且因為隱私和連線穩定性的問題無法連接到雲端。因此，我們需要一個新穎並且可以執行AI應用程式的IoT 閘道器設計。我們提出了3.5層式邊緣運算架構AIoT(AI Internet of Things）邊緣運算（Edge Computing）閘道器架構，這個架構利用了嵌入式硬體以及微服務架構（Microservice）提供了彈性及可擴展AIoT服務，並且可以容納各種不同的AI硬體和軟體佈局,這是傳統工業物聯網閘道器所無法提供的。最後需要用跨攝影機的生物識別技術作為這個架構的應用驗證,我們選擇了在這個架構上同時執行步態辨識和行人重識別應用。測試結果顯示，我們的3.5層 AIoT EC Gateway，可以隨時調整硬體規模，支援不同的應用架構，也可以採用不同的軟體佈局或是接入異質硬體設備以提供AI加速服務，並且提供比高階AI加速器更好的能效。

摘要(英)

With the increase in the number of Internet of Things (IoT) devices that produce video and image data and advanced image recognition applications, as well as the privacy requirements that follow, the computing resources and hardware provided by conventional industrial IoT gateways can no longer meet the requirements of various AI software. In cross-camera biometric technology, considerable artificial intelligence (AI) computing resources are consumed, a requirement exists for adjusting the size of the gateway for different applications and scales, and the software cannot connect to the cloud because of privacy and connection stability issues. Therefore, a novel IoT gateway design that can run AI applications is required. We propose a 3.5-tier AI Internet of Things (AIoT) edge computing (EC) gateway architecture. This architecture utilizes embedded hardware and microservice architecture to provide flexibility and scalability to extend AIoT services, and can accommodate various AI hardware and software topologies, which conventional industrial IoT gateways cannot provide. We are also required to use cross-camera biometric technology for the application verification of this architecture. We chose to run gait recognition and person re-identification (Re-ID) applications on this architecture simultaneously. The test results prove that our 3.5-tier AIoT EC gateway hardware scale can be adjusted at any time to support different application architectures, can adopt different software topologies or heterogeneous hardware devices to provide AI acceleration services, and can provide better energy efficiency services than those of high-end AI accelerators hardware.

關鍵字(中)

★ 邊緣運算
★ 物聯網
★ 閘道器
★ 步態辨識
★ 行人重識別
★ 人工智慧

關鍵字(英)

★ Edge Computing
★ Internet of things
★ gateway
★ Gait recognition
★ Person re-Identification
★ artificial intelligence

論文目次

摘要 i
ABSTRACT ii
誌謝 iii
Table of Contents iv
List of Tables viii
Chapter 1. Introduction 1
1.1. Embedded Intelligent Image Analysis 2
1.2. Biometric Technology 4
1.3. AIoT and EC Gateway 7
1.4. Challenge and Research Motivation 10
1.5. Research Objectives 13
Chapter 2. Person Re-ID, Gait Recognition and AIoT EC Gateway 14
2.1. EC Introduction 15
2.2. Fusion of IoT, AI, and EC 19
2.3. Virtualization and Container Technology 21
2.4. Microservice Architecture 23
2.5. The Fusion of IoT, AI, and EC 25
2.6. Gait Recognition 27
2.7. Person Re-ID 32
CHAPTER 3. 3.5-Tier AIoT EC Gateway Architecture 36
3.1. Design of a 3.5-Tier AIoT EC Gateway 36
3.2. IEFO0 Introduce and system design purpose 40
3.3. Kubernetes 45
3.4. Load Balancing 48
3.5. gRPC and ProtocolBuf 50
3.6. Linkerd 52
3.7. Gaitset 55
3.8. Torchreid 57
3.9. OSNet 58
Chapter 4. Gait Recognition and Person Re-ID System Design on 3.5-Tier AIoT EC Gateway Validation 62
4.1. 3.5-Tier AIoT EC Gateway Architecture and Hardware Environment 64
4.2. AIoT EC Gateway Architecture Performance Test 67
4.3. Alternative Topology for AIoT EC Gateway Performance Test 71
4.4. Hybrid CPU Architecture for AIoT EC Gateway Performance Test 73
4.5. Power Consumption 75
Chapter 5. Conclusions and Future Work 78
5.1. Conclusions 78
5.2. Future Work 79
References 81

參考文獻

[1] E. Cosgrove, "One Billion Surveillance Cameras Will Be Watching around the World in 2021, a New Study Says," CNBC, December, vol. 6, 2019.
[2] A. Castiglione et al., "Walking on the Cloud: Gait Recognition, a Wearable Solution," International Conference on Network and System Security, pp. 174-186, Hong Kong, 27-29 Aug 2018.
[3] N. Abbas et al., "Mobile edge computing: A survey," IEEE Internet of Things Journal, vol. 5, no. 1, pp. 450-465, 2017.
[4] H.-T. Pham et al., "AIoT Solution Survey and Comparison in Machine Learning on Low-cost Microcontroller," 2019 International Symposium on Intelligent Signal Processing and Communication Systems, pp. 1-2, Taipei, 3-6 Dec 2019.
[5] C.-H. Chen and C.-T. Liu, "A 3.5-tier container-based edge computing architecture," Computers & Electrical Engineering, vol. 93, p. 107227, 2021.
[6] P. Hu et al., "Starfish: resilient image compression for AIoT cameras," Proceedings of the 18th Conference on Embedded Networked Sensor Systems, pp. 395-408, Virtual Event Japan, 16-19 Nov 2020.
[7] A. Sepas-Moghaddam and A. Etemad, "Deep Gait Recognition: A Survey," arXiv preprint arXiv:2102.09546, 2021.
[8] A. S. Alharthi et al., "Deep learning for monitoring of human gait: A review," IEEE Sensors Journal, vol. 19, no. 21, pp. 9575-9591, 2019.
[9] J. P. Singh et al., "Vision-based gait recognition: A survey," IEEE Access, vol. 6, pp. 70497-70527, 2018.
[10] J. P. Singh et al., "A survey of behavioral biometric gait recognition: current success and future perspectives," Archives of Computational Methods in Engineering, vol. 28, no. 1, pp. 107-148, 2021.
[11] M. Ye et al., "Deep learning for person re-identification: A survey and outlook," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
[12] B. Omoniwa et al., "Fog/edge computing-based IoT (FECIoT): Architecture, applications, and research issues," IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4118-4149, 2018.
[13] P. Bellavista et al., "A survey on fog computing for the Internet of Things," Pervasive and mobile computing, vol. 52, pp. 71-99, 2019.
[14] M. Crăciunescu et al., "IIoT Gateway for Edge Computing Applications," Proceedings of Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future 2019 pp. 220-231, Cham, 3–4 Oct 2019.
[15] J. Lin et al., "A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications," IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1125-1142, 2017.
[16] X. Chen et al., "Exploiting massive D2D collaboration for energy-efficient mobile edge computing," IEEE Wireless Communications, vol. 24, no. 4, pp. 64-71, 2017.
[17] C.-H. Chen et al., "Edge computing gateway of the industrial internet of things using multiple collaborative microcontrollers," IEEE Network, vol. 32, no. 1, pp. 24-32, 2018.
[18] L. M. Dinca and G. P. Hancke, "The fall of one, the rise of many: a survey on multi-biometric fusion methods," IEEE Access, vol. 5, pp. 6247-6289, 2017.
[19] E. L. Oliveira et al., "Fusion of face and gait for biometric recognition: Systematic literature review," Proceedings of the XII Brazilian Symposium on Information Systems on Brazilian Symposium on Information Systems: Information Systems in the Cloud Computing Era-Volume 1, pp. 108-115, Florianopolis, 17-20 May 2016.
[20] C.-H. Chen et al., "High-level modeling and synthesis of smart sensor networks for Industrial Internet of Things," Computers & Electrical Engineering, vol. 61, pp. 48-66, 2017.
[21] M. Satyanarayanan, "The emergence of edge computing," Computer, vol. 50, no. 1, pp. 30-39, 2017.
[22] P. Porambage et al., "Survey on multi-access edge computing for internet of things realization," IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2961-2991, 2018.
[23] J. Pan and J. McElhannon, "Future edge cloud and edge computing for internet of things applications," IEEE Internet of Things Journal, vol. 5, no. 1, pp. 439-449, 2017.
[24] W. Shi et al., "Edge computing: Vision and challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637-646, 2016.
[25] Z. Zhang et al., "Satellite mobile edge computing: Improving QoS of high-speed satellite-terrestrial networks using edge computing techniques," IEEE Network, vol. 33, no. 1, pp. 70-76, 2019.
[26] H. Li et al., "Learning IoT in edge: Deep learning for the Internet of Things with edge computing," IEEE Network, vol. 32, no. 1, pp. 96-101, 2018.
[27] T. X. Tran et al., "Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges," IEEE Communications Magazine, vol. 55, no. 4, pp. 54-61, 2017.
[28] J. Zhou et al., "Katib: A distributed general AutoML platform on Kubernetes," 2019 USENIX Conference on Operational Machine Learning, pp. 55-57, Santa Clara, 20 May 2019.
[29] Z. Zhou et al., "Robust mobile crowd sensing: When deep learning meets edge computing," IEEE Network, vol. 32, no. 4, pp. 54-60, 2018.
[30] C.-C. Chang et al., "Signature gateway: Offloading signature generation to IoT gateway accelerated by GPU," IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4448-4461, 2018.
[31] S. Sultan et al., "Container security: Issues, challenges, and the road ahead," IEEE Access, vol. 7, pp. 52976-52996, 2019.
[32] R. Chandramouli and R. Chandramouli, Security assurance requirements for linux application container deployments. US Department of Commerce, National Institute of Standards and Technology, 2017.
[33] P. Jamshidi et al., "Microservices: The journey so far and challenges ahead," IEEE Software, vol. 35, no. 3, pp. 24-35, 2018.
[34] N. Dragoni et al., "Microservices: yesterday, today, and tomorrow," Present and ulterior software engineering, pp. 195-216, 2017.
[35] S. Taherizadeh et al., "A capillary computing architecture for dynamic internet of things: Orchestration of microservices from edge devices to fog and cloud providers," Sensors, vol. 18, no. 9, p. 2938, 2018.
[36] L. Chen, "Microservices: Architecting for continuous delivery and devops," 2018 IEEE International Conference on Software Architecture, pp. 39-397, Seattle, 30 Apr-4 May 2018.
[37] P.-H. Tsai et al., "Distributed analytics in fog computing platforms using TensorFlow and Kubernetes," 2017 19th Asia-Pacific Network Operations and Management Symposium, pp. 145-150, Seoul, 27-29 Sep 2017.
[38] A. Boltunov et al., "Modelling a distributed computing system for BigData processing and managing algorithms implementation in MicroGrids," 2019 2nd International Youth Scientific and Technical Conference on Relay Protection and Automation, pp. 1-15, Moscow, 24-25 Oct 2019.
[39] C. Gong et al., "Intelligent cooperative edge computing in internet of things," IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9372-9382, 2020.
[40] T.-C. Chiu et al., "Semisupervised Distributed Learning With Non-IID Data for AIoT Service Platform," IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9266-9277, 2020.
[41] D. Levine et al., Whittle′s Gait Analysis-E-Book. Elsevier health sciences, 2012.
[42] A. Muro-De-La-Herran et al., "Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications," Sensors, vol. 14, no. 2, pp. 3362-3394, 2014.
[43] X. Li et al., "Gait components and their application to gender recognition," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 38, no. 2, pp. 145-155, 2008.
[44] J. Lu and Y.-P. Tan, "Gait-based human age estimation," IEEE Transactions on Information Forensics and Security, vol. 5, no. 4, pp. 761-770, 2010.
[45] R. J. Weiss et al., "Gait pattern in rheumatoid arthritis," Gait & posture, vol. 28, no. 2, pp. 229-234, 2008.
[46] A. Saad et al., "Detection of freezing of gait for Parkinson’s disease patients with multi-sensor device and Gaussian neural networks," International Journal of Machine Learning and Cybernetics, vol. 8, no. 3, pp. 941-954, 2017.
[47] J. M. Montepare et al., "The identification of emotions from gait information," Journal of Nonverbal Behavior, vol. 11, no. 1, pp. 33-42, 1987.
[48] D. Gafurov, "A survey of biometric gait recognition: Approaches, security and challenges," Annual Norwegian computer science conference, pp. 19-21, Oslo, 19-21 Nov 2007.
[49] M. Otero, "Application of a continuous wave radar for human gait recognition," Signal Processing, Sensor Fusion, and Target Recognition XIV, vol. 5809, pp. 538-548, 25 May 2005.
[50] S. Yu and R. Liao, "Weizhi An, Haifeng Chen, Edel B. García, Yongzhen Huang, and Norman Poh. Gaitganv2: Invariant gait feature extraction using generative adversarial networks," Pattern Recognition, vol. 87, pp. 179-189, 2019.
[51] Z. Zhang et al., "On learning disentangled representations for gait recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
[52] A. Zhao et al., "Spidernet: A spiderweb graph neural network for multi-view gait recognition," Knowledge-Based Systems, vol. 206, p. 106273, 2020.
[53] Y. Zhang et al., "Cross-view gait recognition by discriminative feature learning," IEEE Transactions on Image Processing, vol. 29, pp. 1001-1015, 2019.
[54] A. Sepas-Moghaddam et al., "Gait Recognition using Multi-Scale Partial Representation Transformation with Capsules," 2020 25th International Conference on Pattern Recognition, pp. 8045-8052, Milan, 10-15 Jan 2021.
[55] A. Bedagkar-Gala and S. K. Shah, "A survey of approaches and trends in person re-identification," Image and vision computing, vol. 32, no. 4, pp. 270-286, 2014.
[56] Z. Zheng et al., "A discriminatively learned cnn embedding for person reidentification," ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 14, no. 1, pp. 1-20, 2017.
[57] D. Cheng et al., "Person re-identification by multi-channel parts-based cnn with improved triplet loss function," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1335-1344, Las Vegas, 27-30 June 2016.
[58] O. Javed et al., "Appearance modeling for tracking in multiple non-overlapping cameras," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 26-33, San Diego, 20-25 June 2005.
[59] D. Yi et al., "Deep metric learning for person re-identification," 2014 22nd International Conference on Pattern Recognition, pp. 34-39, Stockholm, 24-28 Aug 2014.
[60] X. Wang et al., "Shape and appearance context modeling," 2007 IEEE 11th international conference on computer vision, pp. 1-8, Rio de Janeiro, 14-21 Oct 2007.
[61] M. Gou et al., "A systematic evaluation and benchmark for person re-identification: Features, metrics, and datasets," IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 3, pp. 523-536, 2018.
[62] Z. Cao et al., "OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields," IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 1, pp. 172-186, 2019.
[63] L. Zheng et al., "Person re-identification in the wild," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1367-1376, Honolulu, 21-26 July 2017.
[64] H. Yao et al., "Deep representation learning with part loss for person re-identification," IEEE Transactions on Image Processing, vol. 28, no. 6, pp. 2860-2871, 2019.
[65] Y. Lin et al., "Improving person re-identification by attribute and identity learning," Pattern Recognition, vol. 95, pp. 151-161, 2019.
[66] C. Su et al., "Deep attributes driven multi-camera person re-identification," European conference on computer vision, pp. 475-491, The Netherlands, 11-14 Oct 2016.
[67] J. Dai et al., "Video person re-identification by temporal residual learning," IEEE Transactions on Image Processing, vol. 28, no. 3, pp. 1366-1377, 2018.
[68] C.-Y. Wang et al., "Real-time video-based person re-identification surveillance with light-weight deep convolutional networks," 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 1-8, Taipei, 18-21 Sep 2019.
[69] L. Zheng et al., "Person re-identification: Past, present and future," arXiv preprint arXiv:1610.02984, 2016.
[70] S. Ding et al., "Deep feature learning with relative distance comparison for person re-identification," Pattern Recognition, vol. 48, no. 10, pp. 2993-3003, 2015.
[71] W. Li et al., "Deepreid: Deep filter pairing neural network for person re-identification," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 152-159, Columbus, 23-38 Jun 2014.
[72] K. He et al., "Deep residual learning for image recognition," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, Las Vegas, 27-30 Jun 2016.
[73] K. Zhou et al., "Omni-scale feature learning for person re-identification," Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3702-3712, Seoul, 27 Oct-2 Nov 2019.
[74] M. Wieczorek et al., "On the Unreasonable Effectiveness of Centroids in Image Retrieval," arXiv preprint arXiv:2104.13643, 2021.
[75] D. Fu et al., "Unsupervised Pre-training for Person Re-identification," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14750-14759, Virtual, 19-25 Jun 2021.
[76] Z. Zhong et al., "Re-ranking person re-identification with k-reciprocal encoding," Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1318-1327, Honolulu, 21-26 Jul 2017.
[77] S. Bai et al., "Re-ranking via metric fusion for object retrieval and person re-identification," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 740-749, Long Beach, 15-20 Jun 2019.
[78] M. Ye et al., "Person reidentification via ranking aggregation of similarity pulling and dissimilarity pushing," IEEE Transactions on Multimedia, vol. 18, no. 12, pp. 2553-2566, 2016.
[79] C.-H. Chen and C.-T. Liu, "Person Re-Identification Microservice over Artificial Intelligence Internet of Things Edge Computing Gateway," Electronics, vol. 10, no. 18, p. 2264, 2021.
[80] V. Kulik and R. Kirichek, "The heterogeneous gateways in the industrial internet of things," 2018 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, pp. 1-5, Moscow, 5-9 Nov 2018.
[81] K. Dolui and S. K. Datta, "Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing," 2017 Global Internet of Things Summit (GIoTS), pp. 1-6, Geneva, 6-9 Jun 2017.
[82] M. Wu et al., "Research on the architecture of Internet of Things," 2010 3rd International Conference on Advanced Computer Theory and Engineering, vol. 5, pp. V5-484-V5-487, Chengdu, 20-22 Aug 2010.
[83] R. J. Mayer, "IDEF0 function modeling," Air Force Systems Command, 1992.
[84] M. Williams et al., "Twisted and HTTP/2," in Expert Twisted: Springer, pp. 339-363, 2019.
[85] Y. Fu et al., "Horizontal pyramid matching for person re-identification," Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, pp. 8295-8302, Honolulu, 27 Jan-1 Feb 2019.
[86] Z. Wu et al., "A comprehensive study on cross-view gait based human identification with deep cnns," IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 2, pp. 209-226, 2016.
[87] K. Zhou and T. Xiang, "Torchreid: A library for deep learning person re-identification in pytorch," arXiv preprint arXiv:1910.10093, 2019.
[88] S. Yu et al., "A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition," 18th International Conference on Pattern Recognition, vol. 4, pp. 441-444, Hong Kong, 20-24 Aug 2006.
[89] Z. Zhu et al., "Gait Recognition in the Wild: A Benchmark," Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14789-14799, Virtual, 11-17 Oct 2021.
[90] B. Blanco-Filgueira et al., "Deep learning-based multiple object visual tracking on embedded system for IoT and mobile edge computing applications," IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5423-5431, 2019.
[91] H. Zhao et al., "Power-aware and performance-guaranteed virtual machine placement in the cloud," IEEE Transactions on Parallel and Distributed Systems, vol. 29, no. 6, pp. 1385-1400, 2018.
[92] D. Tsirogiannis et al., "Analyzing the energy efficiency of a database server," Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 231-242, New York, 6-10 Jun 2010.

指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2022-1-12

推文