Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Designing and Implementing Data Warehouse for Agricultural Big Data Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Towards Detection of Abnormal Vehicle Behavior Using Traffic Cameras

verfasst von : Chen Wang, Aibek Musaev, Pezhman Sheinidashtegol, Travis Atkison

Erschienen in: Big Data – BigData 2019

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Throughout the world, many surveillance cameras are being installed every month. For example, there are over 18,000 publicly accessible traffic cameras in 200 cities and metropolitan areas in the United States alone. Live video streams provide real-time big data about behavior happening in the present, such as traffic information. However, until now, extracting intelligence from video content has been mostly manual, i.e. through human observation. The development of smart real-time tools that can detect abnormal vehicle behaviors may alert law enforcement and transportation agencies of possible violators and can potentially avoid traffic accidents. In this study, we address this problem by developing an application for detection of abnormal driving behavior using traffic video streams. Evaluation is performed using real videos from traffic cameras to detect stalled vehicles and possible abnormal vehicle behavior.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Multi-step Short Term Traffic Flow Forecasting Using Temporal and Spatial Data
Literatur
1.
Abbas, Z., et al.: Short-term traffic prediction using long short-term memory neural networks. In: 7th IEEE International Congress on Big Data, BigData Congress 2018, 2 July 2018 through 7 July 2018, pp. 57–65. IEEE (2018)
2.
Asha, C.S., Narasimhadhan, A.V.: Vehicle counting for traffic management system using YOLO and correlation filter. In: 2018 IEEE International Conference on Electronics, Computing and Communication Technologies, vol. 1, pp. 1–6, March 2018
3.
Banharnsakun, A., Tanathong, S.: A hierarchical clustering of features approach for vehicle tracking in traffic environments. Int. J. Intell. Comput. Cybern. 9(4), 354–368 (2016)CrossRef
4.
5.
Cai, Y., et al.: Trajectory-based anomalous behaviour detection for intelligent traffic surveillance. IET Intell. Transp. Syst. 9, 810–816 (2015)CrossRef
6.
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–575 (2003)CrossRef
7.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: International Conference on Computer Vision & Pattern Recognition, vol. 1, pp. 886–893, June 2005
8.
Datondji, S.R.E., et al.: A survey of vision-based traffic monitoring of road intersections. IEEE Trans. Intell. Transp. Syst. 17(10), 2681–2698 (2016)CrossRef
9.
Girshick, R.B.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, 7–13 December 2015, pp. 1440–1448 (2015)
10.
Girshick, R.B., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, 23–28 June 2014, pp. 580–587 (2014)
11.
Hosang, J.H., et al.: What makes for effective detection proposals? IEEE Trans. Pattern Anal. Mach. Intell. 38(4), 814–830 (2016)CrossRef
12.
Howden, L.M., Meyer, J.A.: Contacts between police and the public, 2010. In: 2010 Census Briefs, pp. 1–16, May 2011
13.
Weiming, H., et al.: Traffic accident prediction using 3-D model-based vehicle tracking. IEEE Trans. Veh. Technol. 53(3), 677–694 (2004)CrossRef
14.
Kamijo, S., et al.: Traffic monitoring and accident detection at intersections. IEEE Trans. Intell. Transp. Syst. 1, 108–118 (2000)CrossRef
15.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)CrossRef
16.
Kyriacou, T., Bugmann, G., Lauria, S.: Vision-based urban navigation procedures for verbally instructed robots. Robot. Auton. Syst. 51(1), 69–80 (2005)CrossRef
17.
Li, C., Hua, T.: Human action recognition based on template matching. Procedia Eng. 15, 2824–2830 (2011)CrossRef
18.
Li, X., et al.: Temporal outlier detection in vehicle traffic data. In: 2009 IEEE 25th International Conference on Data Engineering, pp. 1319–1322, March 2009
19.
20.
Lv, Y., et al.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
21.
22.
23.
Redmon, J., et al.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27–30 June 2016, pp. 779–788 (2016)
24.
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)CrossRef
25.
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2017)CrossRef
26.
27.
Saiprasert, C., Pattara-Atikom, W.: Smartphone enabled dangerous driving report system. In: 46th Hawaii International Conference on System Sciences, HICSS 2013, Wailea, HI, USA, 7–10 January 2013, pp. 1231–1237 (2013)
28.
Sivaraman, S., Morris, B., Trivedi, M.: Learning multi-lane trajectories using vehicle-based vision. In: 2011 IEEE International Conference on Computer Vision Workshops, pp. 2070–2076, November 2011
29.
Smal, I., et al.: Multiple object tracking in molecular bioimaging by Rao-Blackwellized marginal particle filtering. Med. Image Anal. 12(6), 764–777 (2008)CrossRef
30.
Sreekumar, U.K., et al.: Real-time traffic pattern collection and analysis model for intelligent traffic intersection. In: 2018 IEEE International Conference on Edge Computing (EDGE), pp. 140–143. IEEE (2018)
31.
32.
Xie, L., et al.: A new CNN-based method for multi-directional car license plate detection. IEEE Trans. Intell. Transp. Syst. 19, 507–517 (2018)CrossRef
33.
Yao, W., et al.: On-road vehicle trajectory collection and scene-based lane change analysis: part ii. IEEE Trans. Intell. Transp. Syst. 18(1), 206–220 (2017)CrossRef
34.
Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13 (2006)CrossRef
35.
Yilmaz, A., Li, X., Shah, M.: Contour-based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1531–1536 (2004)CrossRef
36.
Yin, S., et al.: Hierarchical Kalman-particle filter with adaptation to motion changes for object tracking. Comput. Vis. Image Underst. 115(6), 885–900 (2011)CrossRef
Metadaten
Titel
Towards Detection of Abnormal Vehicle Behavior Using Traffic Cameras
verfasst von
Chen Wang
Aibek Musaev
Pezhman Sheinidashtegol
Travis Atkison
Copyright-Jahr
2019
Buch
Big Data – BigData 2019

Print ISBN: 978-3-030-23550-5

Electronic ISBN: 978-3-030-23551-2

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-23551-2_9

Premium Partner

    Bildnachweise