Notice: Undefined index: linkPowrot in C:\wwwroot\wwwroot\publikacje\publikacje.php on line 1275
Publikacje
Pomoc (F2)
[42532] Artykuł:

Public Transport Vehicle Detection Based on Visual Information

Czasopismo: Communications in Computer and Information Science, Springer, Cham   Tom: 429, Strony: 16-28
ISSN:  1865-0929
ISBN:  978-3-319-07569-3
Wydawca:  SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
Opublikowano: Czerwiec 2014
Seria wydawnicza:  Communications in Computer and Information Science
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Procent
udziału
Liczba
punktów
Mikołaj LeszczukAkademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie20.00  
Remigiusz Baran orcid logoWEAiIKatedra Informatyki, Elektroniki i Elektrotechniki *2015.00  
Łukasz SkoczylasAkademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie20.00  
Mariusz RychlikWyższa Szkoła Technik Komputerowych i Telekomunikacji w Kielcach; Wydział Teleinformatyki20.00  
Przemysław ŚlusarczykUniwersytet Jana Kochanowskiego w Kielcach; Wydział Matematyczno-Przyrodniczy20.00  

Grupa MNiSW:  Materiały z konferencji międzynarodowej (zarejestrowane w Web of Science)
Punkty MNiSW: 15
Klasyfikacja Web of Science: Proceedings Paper


Pełny tekstPełny tekst     DOI LogoDOI     Web of Science Logo Web of Science    
Keywords:

Computer vision  Object detection  Haar-like features  Colour filter 



Abstract:

Freedom of movement is a major challenge for blind or visually impaired people. Movement in urban environment is for such people a big problem. Hence, the aim of the study presented in this paper is to develop an efficient method for identification of different public transport means. To simplify the solution as well as to avoid the need of infrastructure changes, recognition approaches based on visual information collected by smartphones have been assumed. According to the above, detectors based on Haar-like features as well as selected image processing algorithm have been prepared and analysed. Results obtained during performed tests have been reported and compared. Effectiveness of examined individual approaches has been discussed and concluded. Finally, an insight to possible future improvements has been also included.



B   I   B   L   I   O   G   R   A   F   I   A
1. http://docs.opencv.org/doc/user_guide/ug_traincascade.html (viewed February 16, 2014)
2. https://www.threadingbuildingblocks.org/ (viewed February 16, 2014)
3. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)CrossRef
4. Chen, X., Yuille, A.: Detecting and reading text in natural scenes. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR (2004)
5. Chen, X., Yuille, A.L.: A time-efficient cascade for real-time object detection: With applications for the visually impaired. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 03 (2005)
6. Emami, S., Ievgen, K., Mahmood, N.: Mastering OpenCV with Practical Computer Vision Projects. Packt Publishing, Limited (2012)
7. Goh, K.M., Mokji, M.M., Abu-Bakar, S.A.R.: Surf based image matching from different angle of viewpoints using rectification and simplified orientation correction. World Academy of Science, Engineering and Technology 68, 1243–1247 (2012)
8. Janowski, L., Kozłowski, P., Baran, R., Romaniak, P., Glowacz, A., Rusc, T.: Quality assessment for a visual and automatic license plate recognition. Multimedia Tools and Applications, 1–18 (2012)
9. Laganière, R.: OpenCV 2 Computer Vision Application Programming Cookbook. Packt Publishing (2011)
10.Leszczuk, M., Skoczylas, L., Dziech, A.: Simple solution for public transport route number recognition based on visual information. In: 2013 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 32–38 (September 2013)
11.Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003)CrossRef
12.Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRef
13.Macmillan, N.A., Creelman, C.D.: Detection Theory - A user’s guide. Lawrence Erlbaum Associates, Mahwah (2005)
14.Mikolajczyk, K., Schmid, C.: Scale and Affine Invariant Interest Point Detectors. International Journal of Computer Vision 60(1), 63–86 (2004)CrossRef
15.Reinius, S.: Object recognition using the opencv haar cascade-classifier on the ios platform (2013)
16.Sanketi, P., Shen, H., Coughlan, J.M.: Localizing blurry and low-resolution text in natural images. In: Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV), WACV 2011, pp. 503–510. IEEE Computer Society, Washington, DC (2011)
17.Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features, pp. 511–518
18.Viola, P., Jones, M.J.: Robust real-time face detection. International Journal Computer Vision (2004)