PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Detection of critical behaviour on roads by vehicle trajectory analysis

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Detecting restricted or security critical behaviour on roads is crucial for safety protection and fluent traffic flow. In the paper we propose mechanisms for the trajectory of moving vehicle analysis using vision-based techniques applied to video sequences captured by road cameras. The effectiveness of the proposed solution is confirmed by experimental studies.
Rocznik
Strony
46--51
Opis fizyczny
Bibliogr. 16 poz.
Twórcy
  • West Pomeranian University of Technology, Faculty of Computer Science and Information Technology, 52 Żołnierska St., 71-210 Szczecin, Poland
  • West Pomeranian University of Technology, Faculty of Computer Science and Information Technology, 52 Żołnierska St., 71-210 Szczecin, Poland
Bibliografia
  • [1] BABAEI P.: Vehicles Behavior Analysis for Abnormality detection by Multi-View Monitoring, International Research Journal of Applied and Basic Sciences 9 (11), 1929-1936, 2015.
  • [2] BRUN L., et al.: Detection of anomalous driving behaviors by unsupervised learning of graphs, International Conference on Advanced Video and Signal Based Surveillance (AVSS), 405-410, 2014.
  • [3] DESHENG W., JIA W.: A new method of vehicle activity perception from live video, International Symposium on Computer Network and Multimedia Technology, 1-4, 2009.
  • [4] DUBUISSON M.P., JAIN A.K.: A Modified Hausdorff distance for object matching. Proceedings of the 12th IAPR International Conference on Pattern Recognition ICPR94, 566-568, Jerusalem, Israel 1994.
  • [5] FORCZMAŃSKI P.: Recognition of occluded traffic signs based on two-dimensional linear discriminant analysis, Archives of Transport System Telematics 6(3), 2013.
  • [6] FORCZMAŃSKI P., MAŁECKI K., Selected Aspects of Traffic Signs Recognition: Visual versus RFID Approach, 13th in Mikulski J. (ed) Activities of Transport Telematics, Springer Verlag, Berlin Heidelberg, CCIS 395, 268-274, 2013.
  • [7] HU W., et al.: Traffic accident prediction using vehicle tracking and trajectory analysis, Intelligent Transportation Systems, 2003 Proceedings, 2003 IEEE , vol.1, 220-225, 2003.
  • [8] JIANG E., WANG X.: Analysis of Abnormal Vehicle Behavior Based on Trajectory Fitting, Journal of Computer and Communications, 3, 13-18, 2015.
  • [9] KOVACIC K., IVANJKO E., GOLD H.: Computer Visio Systems in Road Vehicles: A Review, Proceedings of the Croatian Computer Vision Workshop, Year 1, 25-30, Zagreb, Croatia, 2013.
  • [10] NOWOSIELSKI A.: Vision-based solutions for driver assistance, Journal of Theoretical and Applied Computer Science 8(4), 35-44, 2014.
  • [11] NOWOSIELSKI A., et al.: Automatic Analysis of Vehicle Trajectory Applied to Visual Surveillance, Image Processing and Communications Challenges 7, Advances in Intelligent Systems and Computing 389, 89-96, 2016.
  • [12] SAUNIER N., et al.: Public video data set for road transportation applications, Transportation Research Board Annual Meeting Compendium of Papers, 14-2379, 2014.
  • [13] SONG H.-S., et al.: Vehicle Behavior Analysis Using Target Motion Trajectories, IEEE Transactions on Vehicular Technology 63(8), 3580-3591, 2014.
  • [14] WANG X., TIEU K., GRIMSON E.: Learning Semantic Scene Models by Trajectory Analysis, Computer Vision – ECCV 2006, LNCS 3952, 110-123, 2006.
  • [15] WU J., et al.: A Survey on Video-based Vehicle Behavior Analysis Algorithms, Journal of Multimedia 7(3), 223-230, 2012.
  • [16] YANG Y., et al.: Trajectory Analysis Using Spectral Clustering and Sequene Pattern Mining, Journal of Computational Information Systems 8(6), 2637-2645, 2012.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a582e2e3-d7e2-4551-8336-1cea7a79d113
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.