Abstract
Parking lot occupation detection using vision systems is a very important task. Many systems use different sensors and their combinations to find out whether the parking lot or space is occupied or not. Using CCTV systems makes it possible to monitor great areas without a need of many sensors. In this paper, we present a method that uses the boosting algorithm for car detection on particular parking spaces and shifting the image to obtain a probability function of car appearance. Using the model of parking lot, we achieve occupancy of each parking space. We also experimented with the detector that is based on the histogram of oriented gradients (HOG) with a support vector machine (SVM). Nevertheless, we found some drawbacks of this detector that we describe in experiments. On the grounds of these drawbacks, we decided to use the AdaBoost based detector.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bileschi, S.M., Leung, B., Rifkin, R.M.: Component-based car detection. Street Scene Images. S.M. Thesis, EECS, MIT (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (June 2005)
Heisele, B., Riskov, I., Morgenstern, C.: Components for object detection and identification. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 225–237. Springer, Heidelberg (2006)
Hinz, S.: Detection and counting of cars in aerial images. In: Proceedings. 2003 International Conference on Image Processing, ICIP 2003, vol. 3, pp.III- 997–III-1000 (September 2003)
Hinz, S., Schlosser, C., Reitberger, J.: Automatic car detection in high resolution urban scenes based on an adaptive 3d-model. In: 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, pp. 167–171 (May 2003)
Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: ECCV Workshop on Statistical Learning in Computer Vision, pp. 17–32 (2004)
Moon, H., Chellappa, R., Rosenfeld, A.: Performance analysis of a simple vehicle detection algorithm. Image and Vision Computing 20(1), 1–13 (2002)
Papageorgiou, C., Poggio, T.: A trainable system for object detection. Int. J. Comput. Vision 38(1), 15–33 (2000)
Ram, T.Z., Zhao, T., Nevatia, R.: Car detection in low resolution aerial images. In: Image and Vision Computing, pp. 710–717 (2001)
Schneiderman, H., Kanade, T.: A statistical method for 3d object detection applied to faces and cars. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 746–751 (2000)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I–511 – I–518 (2001)
Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Proceedings of Ninth IEEE International Conference on Computer Vision, vol. 2, pp. 734–741 (October 2003)
Zhao, T., Nevatia, R.: Car detection in low resolution aerial images. Image and Vision Computing 21(8), 693–703 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Fusek, R., Mozdřeň, K., Šurkala, M., Sojka, E. (2013). AdaBoost for Parking Lot Occupation Detection. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_67
Download citation
DOI: https://doi.org/10.1007/978-3-319-00969-8_67
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00968-1
Online ISBN: 978-3-319-00969-8
eBook Packages: EngineeringEngineering (R0)