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Detection and classification of vehicles using selected methods of image processing

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Methods of vehicles detection and classification using image processing are becoming increasingly popular, especially due to their non-invasiveness in the road surface and relatively lower installation and maintenance costs. These methods are commonly used in traffic flow monitoring systems and detection of vehicles with specific parameters. Importantly, the use of video analytics methods is still characterized by sensitivity to external disturbances such as variable weather conditions. The work discusses selected data processing mechanisms that have been applied within the functioning vehicle recognition subsystem. As part of the analysis, the effectiveness of the applied solutions and sensitivity to the occurring weather conditions were assessed.
Rocznik
Strony
10--14
Opis fizyczny
Bibliogr. 14 poz.
Twórcy
  • APM PRO SP. Z O.O. Barska 70, 43-300 Bielsko-Biała, Poland
autor
  • APM PRO SP. Z O.O. Barska 70, 43-300 Bielsko-Biała, Poland
autor
  • UNIVERSITY OF BIELSKO-BIALA, Willowa 2, 43-309 Bielsko-Biała, Poland
Bibliografia
  • [1] RYGUŁA A., et al.: A Method of Vehicle Classification Using Discriminant Analysis, Archives of Transport System Telematics, vol. 10, pp. 28-31, 2016
  • [2] LOGA W., BRZOZOWSKI K., RYGUŁA A.: A method of vehicle classification using neural networks. Transport Means 2018: Part 1. Proceedings Kaunas University of Technology: Trakai, pp. 263-266, 2018
  • [3] KAFAI M., BHANU B.: Dynamic Bayesian Networks for Vehicle Classification in Video, IEEE Transactions on Industrial Informatics, vol. 8, no. 1, Feb, pp. 100-109, 2012
  • [4] HUIYUAN F., et al.: A vehicle classification system based on hierarchical multi-SVMs in crowded traffic scenes. Neurocomput. 211, pp. 182-190, 2016
  • [5] BAUTISTA C.M., et al.: Convolutional neural network for vehicle detection in low resolution traffic videos, 2016 IEEE Region 10 Symposium (TENSYMP), Bali, pp. 277-281, 2016
  • [6] RAHMAN C.A., BADAWY W., RADMANESH A.: A real time vehicle’s license plate recognition system, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003., Miami, FL, USA, pp. 163-166, 2003
  • [7] SARKER M., et al.: License Plate Detection Based on Haar-like Features and Adaboost Algorithm. Conference: Proceedings of KISM Spring Conference 2013, At Suncheon, Korea, Volume: Vol. 2, No.1, 2013
  • [8] VIOLA P., JONES M.: Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, Kauai, HI, USA, 2001, pp. I-I
  • [9] JAZAYERI A., et al.: Vehicle Detection and Tracking in Car Video Based on Motion Model, IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 583-595, 2011
  • [10] WEN X., et al.: Efficient Feature Selection and Classification for Vehicle Detection, IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 3, pp. 508-517, 2015
  • [11] TSAI L., HSIEH J., FAN K.: Vehicle Detection Using Normalized Color and Edge Map, IEEE Transactions on Image Processing, vol. 16, no. 3, pp. 850-864, 2007
  • [12] OpenCV, Face Detection using Haar Cascades, https://docs.opencv.org/3.4.3/d7/d8b/tutorial_py_face_detection.html, [date of access 10.01.2019]
  • [13] OpenALPR, Automatic License Plate Recognition library, https://github.com/openalpr/openalpr [date of access 10.01.2019]
  • [14] TensorFlow Guide, An open source machine learning framework for everyone, https://www.tensorflow.org/guide [date of access 10.01.2019]
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d5e124c6-96cd-43a9-9302-1754cc556099
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