Abstract
Advancement in technology causes the rise in smart systems. City authorities want to make their cities smarter by making an intelligent decision at real time without the involvement of humans. Monitoring and controlling city traffic is one of the major challenges faced by the authorities. These days, city traffic is monitored by static network cameras deployed on few places of highways. Most of the vehicles are also equipped with cameras to store the videos as a black box. However, monitoring and controlling city traffic by using these thousands of cameras produce an overwhelming amount of high-speed videos, which is challenging to process at real time. Therefore, in this paper, we proposed a system to control city traffic by identifying illegal traffic behaviour, such as illegal U-turn, through continuous monitoring of city traffic. The continuous city traffic is monitored by the network static cameras placed on the road as well as by all the vehicles’ cameras. An architecture is proposed to handle high-speed vast volume of real-time videos efficiently. For that, the two-level parallelism is achieved with the combination of Hadoop and graphics processing unit (GPU) while processing each frame using parallel environment of Hadoop and each block of a frame using GPU. MapReduce Hadoop programming paradigm is not suitable for real-time processing. Thus, we proposed a parameter calculation algorithm that is equivalent to MapReduce mechanism for image processing while dividing the images/frames into fixed-size blocks. We analyzed the city road traffic, which is collected by static cameras placed on various roads and also by vehicles’ cameras while running on the road. Later, the illegal traffic behaviour are recognized, e.g. illegal U-turn, drunken drive, zig-zag drive, over-speed, etc. Finally, the efficiency of the designed system and algorithms are tested by considering the overall running time and system’s throughput with respect to video duration as well as the number of frames. The findings indicate that the proposed architecture with GPU-based algorithm over the Hadoop system perform extraordinary.
Similar content being viewed by others
References
Ahmad A, Paul A, Rathore MM, Chang H (2016) Smart cyber society: integration of capillary devices with high usability based on Cyber-Physical System. Future Gener Comput Syst 56:493–503
Ailamaki NK, Govindaraju S, Harizopoulos, Manocha D (2006) Query co-processing on commodity processors. In: Proceedings of the 32nd international conference on very large data bases (VLDB)
Aly M (2008) Real time detection of lane markers in urban streets. In: Intelligent vehicles symposium. IEEE, pp 7–12
Apache Hadoop (2016) Welcome to Apache \(^{{\rm TM}}\) Hadoop\({\textregistered }\)!. http://hadoop.apache.org/. Accessed 01 Nov 2016
Arlingtonva.us (2016) Live Traffic Cameras. https://transportation.arlingtonva.us/live-traffic-cameras/. Accessed on November 01, 2016
Ashok Kumar PM, Sathya V, Vaidehi (2015) Traffic rule violation detection in traffic video surveillance. Int J Comput Sci Electron Eng (IJCSEE) 3(4):302–307
Benezeth Y, Jodoin PM., Saligrama V, Rosenberger C (2009) Abnormal events detection based on spatio-temporal co-occurences. In: IEEE conference on computer vision and pattern recognition, CVPR 2009. IEEE, pp 2458–2465
Boiman O, Irani M (2007) Detecting irregularities in images and in video. Int J Comput Vis 74(1):17–31
Borkar A, Hayes M, Smith MT (2012) A novel lane detection system with efficient ground truth generation. IEEE Trans Intell Transp Syst 13(1):365–374
Caraffi C, Vojíř T, Trefný J, Šochman J, Matas J (2012) A system for real-time detection and tracking of vehicles from a single car-mounted camera. In: 2012 15th international IEEE conference on intelligent transportation systems, pp 975–982
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
Chen Z et al (2016a) Vehicle detection in high-resolution aerial images via sparse representation and superpixels. IEEE Trans Geosci Remote Sens 54(1):103–116
Chen Z et al (2016b) Vehicle detection in high-resolution aerial images based on fast sparse representation classification and multiorder feature. IEEE Trans Intell Transp Syst 17(8):2296–2309
Cheng H-Y, Weng C-C, Chen Y-Y (2012) Vehicle detection in aerial surveillance using dynamic Bayesian networks. IEEE Trans Image Process 21(4):2152–2159
Collins RT, Liphn AJ, Kanade T, Fujiyoshi H, Duggins D, Tsin Y, Tolliver D, Enomoto N, Hasegawa O., Burt P, Wixson L (2000) A system for video surveillance and monitoring. Technical Report CMU-RI- TR-00- 12. Robotics Inst., Carnegie Mellon Univ
Cui L, Li K, Chen J, Li Z (2011) Abnormal event detection in traffic video surveillance based on local features. In: 2011 4th international congress on image and signal processing (CISP), vol 1. IEEE, pp 362–366
Dean J, Ghemawat S (2004) Mapreduce: simplified data processing on large clusters. In: Proceedings of the sixth conference on symposium on opearting systems design and implementation (OSDI)
Ding Z, Yang B, Chi Y, Guo L (2016) Enabling smart transportation systems: a parallel spatio-temporal database approach. IEEE Trans Comput 65(5):1377–1391. https://doi.org/10.1109/TC.2015.2479596.
Earth Cam (2016) LIVE Webcam Network. http://www.earthcam.com/. Accessed 01 Nov 2016
Fu M, Wang X, Ma H, Yang Y, Wang M (2014) Multi-lanes detection based on panoramic camera. In: 11th IEEE international conference on control and automation (ICCA). IEEE, pp 655–660
Giroux S, Pigot H (2005) From smart homes to smart care. In: ICOST 2005, 3rd international conference on smart homes and health telematics, vol 15. IOS Press
Hadoop (2010). http://ati.amd.com/technology/streamcomputing/
Haloi M, Jayagopi DB (2015) A robust lane detection and departure warning system. In: Proceedings of 2015 IEEE intelligent vehicles symposium (IV), pp 126–131
Hillel AB, Lerner R, Levi D, Raz G (2014) Recent progress in road and lane detection: a survey. Mach Vis Appl 25(3):727–745
Jin J, Gubbi J, Marusic S, Palaniswami M (2014) An information framework for creating a smart city through internet of things. IEEE Internet Things J 1(2):112–121
Kalal Z, Matas J, Mikolajczyk K (2008) Weighted sampling for large-scale boosting. In: BMVC, pp 1–10
Kembhavi A, Harwood D, Davis LS (2011) Vehicle detection using partial least squares. IEEE Trans Pattern Anal Mach Intell 33(6):1250–1265
Kluckner S, Pacher G, Grabner H, Bischof H, Bauer J (2007) A 3d teacher for car detection in aerial images. In: Proceedings of IEEE international conference on computer vision, pp 1–8
Kumar P, Ranganath S, Weimin H, Sengupta K (2005) Framework for real-time behavior interpretation from traffic video. IEEE Trans Intell Transp Syst 6(1):43–53
Leitloff J, Rosenbaum D, Kurz F, Meynberg O, Reinartz P (2014) An operational system for estimating road traffic information from aerial images. Remote Sens 6(11):11315–11341
NVIDIA ACCELERATED COMPUTING (2016) CUDA Toolkit 8.0. https://developer.nvidia.com/cuda-downloads. Accessed 01 Nov 2016
Open Source Computer Vision (3.1.0, 2016). cv::cuda::CascadeClassifie Class Reference. http://docs.opencv.org/3.1.0/d9/d80/classcv_1_1cuda_1_1CascadeClassifier.html. Accessed 01 Nov 2016
Ozgunalp U, Fan R, Ai X, Dahnoun N (2017) Multiple lane detection algorithm based on novel dense vanishing point estimation. IEEE Trans Intell Transp Syst 18(3):621–632
Přibyl O (2015) Transportation, intelligent or smart? On the usage of entropy as an objective function. In: Smart cities symposium prague (SCSP), 2015, pp 1–5
Ranger, C, Raghuraman, R, Penmetsa, A, Bradski, G, Kozyrakis C (2007) Evaluating Mapreduce for multi-core and multiprocessor systems. In: Proceedings of IEEE 13th international symposium on high performance computer architecture (HPCA)
Rathore MM, Ahmad A, Paul A, Jeon G (2015) Efficient graph-oriented smart transportation using internet of things generated big data. In: 2015 11th international conference on signal-image technology and internet-based systems (SITIS), pp 512–519
Rathore MM, Ahmad A, Paul A, Rho S (2016) Urban planning and building smart cities based on the internet of things using big data analytics. Comput Netw 101:63–80
Rathore MM, Son H, Ahmad A, Paul A, Jeon G (2017) Real-time big data stream processing using GPU with spark over Hadoop ecosystem. Int J Parallel Programm 1–17
Reynolds D (2015) Gaussian mixture models. In: Li SJ, Jain A (eds) Encyclopedia of biometrics. Springer, Berlin, pp 827–832
Shao W, Yang W, Liu G, Liu J (2012) Car detection from high-resolution aerial imagery using multiple features. In: Proceedings of IEEE International symposium on Geoscience Remote Sensing, pp 4379–4382
Sochman J, Matas J (2005) Waldboost-learning for time constrained sequential detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 2, pp 150–156
Song CH, Lee J (2014) Detection of illegal u-turn vehicles by optical flow analysis. J Korean Inst Commun Inf Sci 39(10):948–956
SPARK Apache (2016) Apache Spark\(^{{\rm TM}}\). http://spark.apache.org/. Accessed on November 01, 2016
Trefný J, Matas J (2010) Extended set of local binary patterns for rapid object detection. In: Proceedings of the computer vision winter workshop, vol 2010
Tuermer S, Kurz F, Reinartz P, Stilla U (2013) Airborne vehicle detection in dense urban areas using hog features and disparity maps. IEEE J Sel Topics Appl Earth Observ Remote Sens 6(6):2327–2337
Vijverberg JA, de Koning NA, Han J, de With PH, Cornelissen D (2007) High-level traffic-violation detection for embedded traffic analysis. In: IEEE international conference on acoustics, speech and signal processing, ICASSP 2007, vol 2. IEEE, pp II–793
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, CVPR 2001, vol 1, pp I–511
Yim YU, Oh SY (2003) Three-feature based automatic lane detection algorithm (TFALDA) for autonomous driving. IEEE Trans Intell Transp Syst 4(4):219–225
Zhu F, Li Z, Chen S, Xiong G (2016) Parallel transportation management and control system and its applications in building smart cities. IEEE Trans Intell Transp Syst 17(6):1576–1585. https://doi.org/10.1109/TITS.2015.2506156
Acknowledgements
This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (NRF-2017R1C1B5017464).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All the authors declares that he/she has no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by M. Anisetti.
Rights and permissions
About this article
Cite this article
Rathore, M.M., Son, H., Ahmad, A. et al. Real-time video processing for traffic control in smart city using Hadoop ecosystem with GPUs. Soft Comput 22, 1533–1544 (2018). https://doi.org/10.1007/s00500-017-2942-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-017-2942-7