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Deep learning based smart traffic management using video analytics and IoT sensor fusion

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Abstract

Due to the rapid growth in population and continue rise in number of vehicle on road issue of transportation congestion arises. Combination of Internet-of-Things-Aided Smart Transportation System is doing promising work in this area. A massive amount of video streaming data is produced at high speed by distributed mobile IoT devices and video cameras due to the use of artificial intelligence (AI) and Internet of Things (IoT) combinations in smart city scenarios. Real-time data processing application demands efficient analysis of these data. The key focus in this work is on improving cloud-based traffic video analytics systems by executing a two-step approach: first, Edge-based pre-processing of a video stream to reduce data transmission time and Cloud-based traffic Video Analytics. Second, Video Analytics and Sensor Fusion (VA/SF) are studied and examined to guarantee that the continuum of potentials are sufficiently covered by the data that algorithms are trained on and make it sufficiently efficient to provide high accuracy or low latency modes of services. We suggest a YOLO based deep learning video analytics system on the cloud to perform real-time object detection for traffic surveillance video. The proposed VA/SF model reduces detection speed of the model while improving the object detection accuracy by 1.8% when compared to no-IoT sensor fusion. The experiment proves that higher accuracy with better detection is achieved by our traffic analytical model under extreme weather conditions.

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We declare that all the data associated with the manuscript is mentioned in the manuscript.

References

  • Abdel-Hakim AE, Farag AA (2006) A SIFT descriptor with color invariant characteristics. In: 2006 IEEE computer society conference on computer vision and pattern recognition, vol 2. IEEE, London, pp 1978–1983

  • Abdullah T, Anjum A, TariqMF, Baltaci Y, Antonopoulos N (2014) Traffic monitoring using video analytics in clouds. In: Proceedings of the 2014 IEEE/ACM7th international conference on utility and cloud computing. IEEE Computer Society, London, pp 39–48

  • Aftab Alam, Young-Koo Lee (2020) TORNADO: intermediate results orchestration based service-oriented data curation framework for intelligent video big data analytics in the cloud. Sensors. https://doi.org/10.3390/s20123581

    Article  Google Scholar 

  • Ahmad K, Khujamatov H, Lazarev A, Usmanova N, Alduailij M, Alduailij M (2023) Internet of Things-aided intelligent transport systems in smart cities: challenges, opportunities, and future. Wirel Commun Mobile Comput 2023:28, Article ID 7989079. https://doi.org/10.1155/2023/7989079

  • Alam A, Ullah I, Lee YK (2020) Video big data analytics in the cloud: a reference architecture, survey, opportunities, and open research issues. In: IEEE Access, vol. 8, pp. 152377–152422. https://doi.org/10.1109/ACCESS.2020.3017135

  • Alam A, Khalid S, Khan MN, Afridi TH, Ullah I, Lee YK (2021) Video big data analytics in the cloud: research issues and challenges. Springer, Cham. https://doi.org/10.1007/978-3-030-66840-2108

    Book  Google Scholar 

  • Ali M, Anjum A, Rana O, Zamani AR, Balouek-Thomert D, Parashar M (2020) RES: real-time video stream analytics using edge enhanced clouds. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2020.2991748

    Article  Google Scholar 

  • Anjum A, Abdullah T, Tariq MF, Baltaci Y, Antonopoulos N (2015) An object detection and classification framework for high performance video analytics. IEEE Trans Cloud Comput 7(4):11521167. https://doi.org/10.1109/TCC.2016.2517653

    Article  Google Scholar 

  • Anjum A, Abdullah T, Tariq M, Baltaci Y, Antonopoulos N (2016) Video stream analysis in clouds: an object detection and classification framework for high performance video analytics. IEEE Trans Cloud Comput 2016:1

    Google Scholar 

  • Apostolo Guilherme H, Bauszat Pablo, Nigade Vinod, Bal Henri E, Wang Lin (2022) Live video analytics as a service. In: EuroMLSys 2022—proceedings of the 2nd European workshop on machine learning and systems, 2nd ed. Association for Computing Machinery, Inc, Rennes, pp 37–44

  • Dadheech A (2018) Preventing information leakage from encoded data in lattice based cryptography. In: 2018 international conference on advances in computing, communications and informatics (ICACCI). IEEE, London, pp 1952–1955

  • Déniz O, Bueno G, Salido J, De la Torre F (2011) Face recognition using histograms of oriented gradients. Pattern Recogn Lett 32(12):1598–1603

    Article  Google Scholar 

  • Fan J, Han F, Liu H (2013) Challenges of big data analysis. Natl Sci Rev 2013:293–314

    Google Scholar 

  • Fragkiadaki E, Anagnostopoulos F, Triliva S (2023) The experience of psychological therapies for people with multiple sclerosis: a mixed-methods study towards a patient-centred approach to exploring processes of change. Counsel Psychother Res. https://doi.org/10.1002/capr.12615

    Article  Google Scholar 

  • Huang Y-Q, Zheng J-C, Sun S-D, Yang C-F, Liu J (2020) Optimized YOLOv3 algorithm and its application in traffic flow detections. Appl Sci 10:3079

    Article  Google Scholar 

  • Huang C et al (2023) DNA synthetic steganography based on conditional probability adaptive coding. IEEE Trans Inform Forensics Secur 18:4747–4759. https://doi.org/10.1109/TIFS.2023.3285045

    Article  Google Scholar 

  • Ikram A, Anjum A, Hill R, Antonopoulos N, Liu L, Sotiriadis S (2015) Approaching the Internet of Things (IoT): a modelling, analysis and abstraction framework. Concurr Comput Pract Exp 27(8):1966–1984

    Article  Google Scholar 

  • Iqbal MH, Soomro TR (2015) Big data analysis: apache storm perspective. Int J Comput Trends Technol 19(1):9–14

    Article  Google Scholar 

  • Jain S, Nguyen V, Gruteser M, Bahl P (2017) Panoptes: servicing multiple applications simultaneously using steerable cameras. In: IPSN, pp 119–130

  • Jin Y, Zhao A (2024) Bert-based graph unlinked embedding for sentiment analysis. Complex Intell Syst 10:2627–2638. https://doi.org/10.1007/s40747-023-01289-9

    Article  Google Scholar 

  • Kafka A (2014) A high-throughput, distributed messaging system, vol 5(1). www.kafka.apache.org

  • Khan AA, Laghari AA, Shaikh AA, Shaikh ZA, Jumani AK (2022) Innovation in multimedia using IoT systems. Multimed Comput Syst Virt Real 2022:171–187

    Article  Google Scholar 

  • Lakhan A, Memon MS, Mastoi Q et al (2022) Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network. Clust Comput 25:2061–2083. https://doi.org/10.1007/s10586-021-03333-0

    Article  Google Scholar 

  • Lin FC, Ngo HH, Dow CR (2020) A cloud-based face video retrieval system with deep learning. J Supercomput 76:8473–8493. https://doi.org/10.1007/s11227-019-03123-x

    Article  Google Scholar 

  • Muhammad K et al (2018) Convolutional neural networks based fire detection in surveillance videos. IEEE Access 6:18174–18183

    Article  MathSciNet  Google Scholar 

  • Park DS (2018) Future computing with IoT and cloud computing. J Supercomput 74(12):6401–6407

    Article  Google Scholar 

  • Pereira R, Azambuja M, Breitman K, Endler M (2010) An architecture for distributed high performance video processing in the cloud. In: 2010 IEEE 3rd international conference on cloud computing (CLOUD). IEEE, pp 482–489

  • Qian X et al (2017) An object tracking method using deep learning and adaptive particle filter for night fusion image. In: 2017 international conference on progress in informatics and computing (PIC), Nanjing, pp 138–142

  • Raina R, Madhavan A, Ng AY (2009) Large scale deep unsupervised learning using graphics processors. In: 26th ACM annual international conference in machine learning, pp 873–880

  • Raman RC, Pankaj S, Santosh G, Akanksha N, Yogendra SA (2023) Design and implementation of a smart traffic management system using Internet of Things (IoT) technology Yogendra Narayan Pankaj. Eur Chem Bull 12:417–434

    Google Scholar 

  • Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA, pp 779–788. https://doi.org/10.1109/CVPR.2016.91

  • Sabokrou M, Fayyaz M, Fathy M, Moayed Z, Klette R (2018) Deep-anomaly: fully convolutional neural network for fast anomaly detection in crowded scenes, vol 172. Elsevier, London, pp 88–97

    Google Scholar 

  • Shan C, Porikli F, Xiang T, Gong S (2012) Video analytics for business Intelligence. Springer, London

    Book  Google Scholar 

  • Spark A (2016) Apache spark: lightning-fast cluster computing. http://spark.apache.org

  • Talaat FM, ZainEldin H (2023) An improved fire detection approach based on YOLO-v8 for smart cities. Neural Comput Appl 35:20939–20954. https://doi.org/10.1007/s00521-023-08809-1

    Article  Google Scholar 

  • Terven J, Cordova-Esparza D (2023) A comprehensive review of YOLO: from YOLOv1 and beyond. arXiv:2304.00501

  • Wang T, Zhao L, Huang P, Zhang X, Xu J (2021) Haze concentration adaptive network for image dehazing. Neurocomputing 439:75–85. https://doi.org/10.1016/j.neucom.2021.01.042. ISSN 0925-2312

  • Wang C-Y, Bochkovskiy A, Liao H-YM (2023) YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv:2207.02696

  • Waqas M, Kumar K, Saeed U, Rind MM, Shaikh AA, Hussain F, Rai A, Qazi AQ (2022) Botnet attack detection in Internet of Things devices over cloud environment via machine learning. Concurr Comput Pract Exp 34(4):e6662

    Article  Google Scholar 

  • Yaseen MU, Anjum A, Antonopoulos N (2016a) Spatial frequency based video stream analysis for object classification and recognition in clouds. In: 2016 IEEE/ACM 3rd international conference on big data computing applications and technologies (BDCAT), pp 18–26

  • Yaseen MU, Zafar MS, Anjum A, Hill R (2016b) High performance video processing in cloud data centres. In: 2016 IEEE symposium on service-oriented system engineering (SOSE), March. IEEE, London, pp 152–161

  • Yaseen MU, Anjum A, Rana O, Hill R (2018a) Cloud-based scalable object detection and classification in video streams. Fut Gener Comput Syst 80:286–298

  • Yaseen MU, Anjum A, Rana O, Antonopoulos N (2018b) Deep learning hyper-parameter optimization for video analytics in clouds. IEEE Trans Syst Man Cybern Syst 2018:1–12

  • Yaseen MU, Anjum A, Rana O, Hill R (2018c) Cloud-based scalable object detection and classification in video streams. Fut Gener Comput Syst 80:286–298. https://doi.org/10.1016/j.future.2017.02.003

  • Yaseen MU, Anjum A, Farid M, Antonopoulos N (2018d) Cloud-based video analytics using convolutional neural networks. Cloud Based Video Anal Syst Softw Pract Exp. https://doi.org/10.1002/spe.2636

  • Yaseen MU, Anjum A, Farid M, Antonopoulos N (2019) Cloudbased video analytics using convolutional neural networks. Softw Pract Exp 49(4):565–583

    Article  Google Scholar 

  • Yaseen MU, Anjum A, Farid M, Antonopoulos N (2019b) Cloudbased video analytics using convolutional neural networks. Softw Pract Exp 49(4):565–583

  • Zamani AR, Zou M, Diaz-Montes J, Petri I, Rana O, Anjum A, Parashar M (2017) Deadline constrained video analysis via in-transit computational environments. IEEE Trans Serv Comput 2017:1

    Google Scholar 

  • Zhang W, Xu L, Duan P, Gong W, Lu Q, Yang S (2015) A video cloud platform combing online and offline cloud computing technologies. Personal Ubiquit Comput 19(7):1099–1110

    Article  Google Scholar 

  • Zhang H, Ananthanarayanan G, Bodik P, Philipose M, Bahl P, Freedman M J (2017) Live video analytics at scale with approximation and delay-tolerance. In: NSDI, vol 9, p 1

  • Zhang J, Haasa C, Hannab S (2021) Comparative study of automatic multi-class object detection algorithms with transfer learning based on a dataset from construction sites. In: 28th international workshop on intelligent computing in engineering

  • Zhu L, Zheng X, Li P, Wang Y (2014) A cloud based object recognition platform for IOS. In: International conference on identification, information and knowledge in the Internet of Things, pp 68–71

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Acknowledgements

The author would like to thank the technical staff of the department for their help in offering the resources in running the program and generating results.

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Correspondence to Madhuri Bhavsar.

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Dadheech, A., Bhavsar, M., Verma, J.P. et al. Deep learning based smart traffic management using video analytics and IoT sensor fusion. Soft Comput 28, 13461–13476 (2024). https://doi.org/10.1007/s00500-024-10382-1

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