Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The work proposes a new method for vehicle classification, which allows treating vehicles uniformly at the stage of defining the vehicle classes, as well as during the classification itself and the assessment of its correctness. The sole source of information about a vehicle is its magnetic signature normalised with respect to the amplitude and duration. The proposed method allows defining a large number (even several thousand) of classes comprising vehicles whose magnetic signatures are similar according to the assumed criterion with precisely determined degree of similarity. The decision about the degree of similarity and, consequently, about the number of classes, is taken by a user depending on the classification purpose. An additional advantage of the proposed solution is the automated defining of vehicle classes for the given degree of similarity between signatures determined by a user. Thus the human factor, which plays a significant role in currently used methods, has been removed from the classification process at the stage of defining vehicle classes. The efficiency of the proposed approach to the vehicle classification problem was demonstrated on the basis of a large set of experimental data.
Czasopismo
Rocznik
Tom
Strony
619--630
Opis fizyczny
Bibliogr. 16 poz., rys., tab., wykr., wzory
Twórcy
autor
- Department of Measurement and Electronics ,AGH – University of Science and Technology, 30 Mickiewicz Ave., 30-059 Cracow, Poland
autor
- Department of Measurement and Electronics ,AGH – University of Science and Technology, 30 Mickiewicz Ave., 30-059 Cracow, Poland
Bibliografia
- [1] Ling, B., Gibson, D., Middleton, D. (2013). Motorcycle Detection and Counting Using Stereo Camera, IR Camera and Microphone Array. Conference on Video Surveillance and Transportation Imaging Applications, Burlingame, CA.
- [2] Guo, B., Nixon, M., Damarla, T. (2012). Improving acoustic vehicle classification by information fusion. Patern analysis and applications, 15, 29-43.
- [3] Gajda, J., Piwowar, P., Sroka, R., Stencel, M., Zeglen, T. (2012). Application of inductive loops as wheel detectors. Transportation research part C - Emerging technologies, 21, 57-66.
- [4] Oh, C., Ritchie, S.G. (2007). Recognizing vehicle classification information from blade sensor signature. Pattern Recognition Letters, 28, 1041-1049.
- [5] Vehicle classification using FHWA 13 category scheme. (2012). Traffic Recorder Instruction Manual, from:http://onlinemanuals.txdot.gov/txdotmanuals/tri/vehicle_classification_using_fhwa_13 category_scheme.htm
- [6] Pursula, M., Kosonen, I. (1989). Microprocessor- and PC-based vehicle classification equipment using induction loops. IEE 2nd Int. Conf. Road Traffic Monit, 24-28.
- [7] Gajda, J., Sroka, R., Stencel, M., Zeglen, T. (2000). An Eastern European example of the identification of moving vehicle parameters using the tried and trusted method of weigh in motion. Traffic Technol. Int., 87-90.
- [8] Sun, C. (2000). An investigation in the use of inductive loop signatures for vehicle classification. Inst. Transp. Stud., Univ. California, Berkeley, CA, California PATH Res. Rep., UCB-ITS-PRR-2000 4.
- [9] Sroka, R., (2004). Data fusion methods based on fuzzy measures in vehicle classification process. 21st IEEE IMTC, 3, 2234-2239.
- [10] Zhang, G. H., Wang, Y. H., Wie, H., (2006). Artificial neural network method for length-based vehicle classification using single-loop outputs. Traffic Urban Data, Transp. Res. Rec., no. 1945, 100-108.
- [11] Ki, Y. K., Baik, D. K. (2006). Vehicle classification algorithm for single loop detectors using neural networks. IEEE Trans. Veh. Technol., 55, 1704-1711.
- [12] Lima, G. R., Silva, J. D., Saotome, O. (2010). Vehicle inductive signatures recognition using a Madaline neural network. Neural Comput & Applic, 19, 421-436.
- [13] Meta, S., Cinsdikici, M. G. (2010). Vehicle-Classification Algorithm Based on Component Analysis for Single-Loop Inductive Detector. IEEE Trans. Veh. Technol, 59, 2795-2805.
- [14] Gajda, J., Sroka, R., Stencel, M., Wajda, A., Zeglen, T. (2001). A vehicle classification based on inductive loop detectors. 18th IEEE IMTC, 1, 460-464.
- [15] Coifman, B., Kim, S. (2009). Speed estimation and length based vehicle classification from freeway singleloop detectors. Transportation Research Part C, 17, 349-364.
- [16] Ali, S., Joshi, N., George, B., Vanajakshi, L. (2012). Application of Random Forest Algorithm to Classify Vehicles Detected by a Multiple Inductive Loop System. 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, Alaska, USA, 16-19.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-198e48aa-df5d-4f32-b875-12329f132979