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Model Free Identification of Traffic Conditions Using Unmanned Aerial Vehicles and Deep Learning

Abstract

The purpose of this paper is to provide a methodological framework to identify traffic conditions based on non-calibrated video recordings captured from unmanned aerial vehicles (UAV) using deep learning. To this end, we propose two complementary to each other approaches: (i) identify in real time, with minimal computational cost, traffic conditions, (ii) localize, classify vehicles and approximate traffic variables (volume, speed, density) on a road segment from video captured by UAVs. Both problems are formulated as classification problems and tackled using Convolutional Neural Networks (CNN). The use of pre-trained CNNs is also investigated. Both approaches are, then, analysed based on their accuracy and feasibility in implementation. Findings indicate that all models developed achieve a detection accuracy of 89% and higher. The CNN with the best performance can classify traffic conditions between constrained and unconstrained traffic with 91% accuracy higher than what a pretrained model achieved and with significantly faster training times. Furthermore, findings indicated that pretrained neural network for traffic localization was able to predict the position and type of vehicles with a precision of 0.91. Based on the fundamental traffic diagram, it was shown that the two approaches provide compatible results and a feasible representation of traffic on the study area. Finally, possible applications in the field of transportation and traffic monitoring are discussed.

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References

  • Ammour N, Alhichri H, Bazi Y, Benjdira B, Alajlan N, Zuair M (2017) Deep learning approach for car detection in UAV imagery. Remote Sens 9(4):312

    Article  Google Scholar 

  • Barmpounakis E, Geroliminis N (2020) On the new era of urban traffic monitoring with massive drone data: the pNEUMA large-scale field experiment. Transp Res Part C Emerg Technol 111:50–71

    Article  Google Scholar 

  • Barmpounakis E, Sauvin GM, Geroliminis N (2020) Lane detection and lane-changing identification with high-resolution data from a swarm of drones. Transp Res Rec J Transp Res Board 2674(7):1–15

    Article  Google Scholar 

  • Barmpounakis EN, Vlahogianni EI, Golias JC (2016) Unmanned Aerial Aircraft Systems for transportation engineering: Current practice and future challenges. Int J Transp Sci Technol 5(3):111–122

    Article  Google Scholar 

  • Barmpounakis EN, Vlahogianni EI, Golias JC, Babinec A (2017) How accurate are small drones for measuring microscopic traffic parameters? Transp Lett 11(6):1–9

    Google Scholar 

  • Barmpounakis E, Vlahogianni E, Golias J (2018) Identifying predictable patterns in the unconventional overtaking decisions of PTW for Cooperative ITS. IEEE Trans Intell Veh 3(1):102–111

    Article  Google Scholar 

  • Benjdira B, Khursheed T, Koubaa A, Ammar A, Ouni K (2019) Car detection using unmanned aerial vehicles: comparison between faster R-CNN and YOLOv3. In: 2019 1st international conference on unmanned vehicle systems-Oman (UVS), IEEE, February, pp 1–6

  • Boyer RS, Moore JS (1991). MJRTY—a fast majority vote algorithm. In Automated reasoning. Springer, Dordrecht, pp 105–117

  • Braut V, Culjak M, Vukotic V, Segvic S, Sevrovic M, Gold H (2012) Estimating OD Matrices at intersections in airborne video—a pilot study. MIPRO, 2012 Proceedings of the 35th International Convention, Opatija, Croatia, 21–25 May 2012, 977–982

  • Canziani A, Paszke A, Culurciello E (2016) An analysis of deep neural network models for practical applications. arXiv preprint.

  • Chakraborty P, Adu-Gyamfi YO, Poddar S, Ahsani V, Sharma A, Sarkar S (2018) Traffic congestion detection from camera images using deep convolution neural networks. Transp Res Rec 2672(45):222–231

    Article  Google Scholar 

  • Chen X, Xiang S, Liu CL, Pan CH (2014) Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci Remote Sens Lett 11(10):1797–1801

    Article  Google Scholar 

  • Coifman B, McCord M, Mishalani RG, Redmill K (2004) Surface transportation surveillance from unmanned aerial vehicles. Proceedings of the 83rd Annual Meeting of the Transportation Research Board. https://www2.ece.ohio-state.edu/~coifman/documents/UAV_paper.pdf

  • Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In Computer vision and pattern recognition, CVPR 2009. IEEE Conference on IEEE, pp 248–255

  • Gao H, Kong SL, Zhou S, Lv F, Chen Q (2014) Automatic extraction of multi-vehicle trajectory based on traffic videotaping from quadcopter model. Appl Mech Mater 552:232–239

    Article  Google Scholar 

  • Gkolias K, Vlahogianni EI (2018) Convolutional neural networks for on-street parking space detection in urban networks. IEEE Trans Intell Transp Syst 20(12):4318–4327

  • Google-Tensorflow (2019) github.com/tensorflow. Retrieved 2019, from TensorFlow-Slim image classification model library. https://github.com/tensorflow/models/tree/master/research/slim

  • Granlund G, Nordberg K, Wiklund J, Doherty P, Skarman E, Sandewall E (2000) WITAS: an intelligent autonomous aircraft using active vision. In: UAV 2000 international technical conference and exhibition. Euro UVS, Paris, France, June 2000

  • Gu X, Abdel-Aty M, Xiang Q, Cai Q, Yuan J (2019) Utilizing UAV video data for in-depth analysis of drivers’ crash risk at interchange merging areas. Accid Anal Prev 123:159–169

    Article  Google Scholar 

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  • Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Murphy K (2017) Speed/accuracy trade-offs for modern convolutional object detectors. Google Research

  • Huval B, Wang T, Tandon S, Kiske J, Song W, Pazhayampallil J et al (2015) An empirical evaluation of deep learning on highway driving. arxiv:1504.01716

  • Kaufmann S, Kerner BS, Hubert Rehborn MK, Klenov SL (2018) Aerial observations of moving synchronized flow patterns in over-saturated city traffic. Transp Res Part C Emerg Technol 86:393–406

    Article  Google Scholar 

  • Ke R, Li Z, Kim S, Ash J, Cui Z, Wang Y (2017) Real-time bidirectional traffic flow parameter estimation from aerial videos. IEEE Trans Intell Transp Syst 18(4):890–901

    Article  Google Scholar 

  • Ke R, Li Z, Tang J, Pan Z, Wang Y (2018) Real-time traffic flow parameter estimation from uav video based on ensemble classifier and optical flow. IEEE Trans Intell Transp Syst 99:1–11

    Google Scholar 

  • Khan MA, Ectorsa W, Bellemansa T, Ruichekb Y, Yasara A-H, Janssensa D, Wets G (2018a) Unmanned aerial vehicle-based traffic analysis: a case study to analyze traffic streams at urban roundabouts. Procedia Comput Sci 130:636–648

    Article  Google Scholar 

  • Khan M, Ectors W, Bellemans T, Janssens D, Wets G (2018b) Unmanned aerial vehicle-based traffic analysis: a case study for shockwave identification and flow parameters estimation at signalized intersections. Remote Sens 10(3):458

    Article  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

  • Kyrkou C, Plastiras G, Theocharides T, Venieris SI, Bouganis CS (2018) DroNet: efficient convolutional neural network detector for real-time UAV applications. In: 2018 design, automation & test in Europe conference & exhibition (DATE). IEEE, pp 967–972

  • Laña I, Sanchez-Medina JJ, Vlahogianni EI, Del Ser J (2021) From data to actions in intelligent transportation systems: a prescription of functional requirements for model actionability. Sensors 21(4):1121. https://doi.org/10.3390/s21041121

    Article  Google Scholar 

  • LeCun Y, Bengio Y (1995) Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361(10):1995

  • Lee, J, Zijia Z, Kitae K, Branislav D, Bo D, Slobodan G (2015) Examining the applicability of small quadcopter drone for traffic surveillance and roadway incident monitoring. Transportation Research Board 94th Annual Meeting Compendium of Papers, pp 15–4184.

  • Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: single shot multibox detector. In: European conference on computer visio. Springer, Cham, pp 21–37

  • Lv Y, Duan Y, Kang W, Li Z, Wang FY (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873

    Google Scholar 

  • Mairaj A, Baba AI, Javaid AY (2019) Application specific drone simulators: recent advances and challenges. Simul Model Pract Theory. https://doi.org/10.1016/j.simpat.2019.01.004

    Article  Google Scholar 

  • Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  • Phon-Amnuaisuk S, Murata KT, Pavarangkoon P, Yamamoto K, Mizuhara T (2018) Exploring the applications of faster R-CNN and single-shot multi-box detection in a smart nursery domain. arxiv:1808.08675

  • Polson NG, Sokolov VO (2017) Deep learning for short-term traffic flow prediction. Transp Res Part C Emerg Technol 79:1–17

    Article  Google Scholar 

  • Puri A (2005) A survey of Unmanned Aerial Vehicles (UAV) for traffic surveillance. University of South Florida, Department of Computer Science and Engineering, pp 1–29

    Google Scholar 

  • Puri A, Valavanis K, Kontitsis M (2007) Statistical profile generation for traffic monitoring using realtime UAV based video data. 2007 Mediterr Conf Control Autom. https://doi.org/10.1109/MED.2007.4433658

    Article  Google Scholar 

  • Reinartz P, Lachaise M, Schmeer E, Krauss T, Runge H (2006) Traffic monitoring with serial images from airborne cameras. ISPRS J Photogramm Remote Sens 61(3):149–158

    Article  Google Scholar 

  • Rezaei M, Isehaghi M (2018) An efficient method for license plate localization using multiple statistical features in a multilayer perceptron neural network. In: 2018 9th conference on artificial intelligence and robotics and 2nd Asia-Pacific international symposium. IEEE, pp 7–13

  • Salvo G, Luigi C, Alessandro S (2014) Urban traffic analysis through an UAV. Procedia Soc Behav Sci 111:1083–1091

    Article  Google Scholar 

  • Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4510–4520

  • Sermanet P, LeCun Y (2011) Traffic sign recognition with multi-scale convolutional networks. In: The 2011 international joint conference on neural networks. IEEE, pp 2809–2813

  • Shastry A, Schowengerdt R (2002) Airborne video registration for visualization and parameter estimation of traffic flows. In: Proceedings of pecora, vol 15, pp 391–405

  • Sivaraman S, Trivedi MM (2013) Looking at vehicles on the road: a survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Trans Intell Transp Syst 14(4):1773–1795

    Article  Google Scholar 

  • Wang L, Chen F, Yin H (2016) Detecting and tracking vehicles in traffic by unmanned aerial vehicles. Autom Constr 72:294–308

    Article  Google Scholar 

  • Wang X, Cheng P, Liu X, Uzochukwu B (2018) Fast and accurate, convolutional neural network based approach for object detection from UAV. In: IECON 2018-44th annual conference of the IEEE industrial electronics society. IEEE, pp 3171–3175

  • Yu F, Xian W, Chen Y, Liu F, Liao M, Madhavan V, Darrell T (2018) BDD100K: a diverse driving video database, University of California, Berkeley

  • Zhang W, Jordan G, Livshits V (2016) Generating a vehicle trajectory database from time-lapse aerial photography. Transp Res Rec 2594:148–158

    Article  Google Scholar 

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Correspondence to Eleni I. Vlahogianni.

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Vlahogianni, E.I., Del Ser, J., Kepaptsoglou, K. et al. Model Free Identification of Traffic Conditions Using Unmanned Aerial Vehicles and Deep Learning. J. Big Data Anal. Transp. 3, 1–13 (2021). https://doi.org/10.1007/s42421-021-00038-z

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  • DOI: https://doi.org/10.1007/s42421-021-00038-z

Keywords

  • Traffic monitoring
  • Traffic state identification
  • Unmanned Aerial Vehicles
  • Computer vision
  • Convolutional neural network
  • Transfer learning