Moving Object Detection, Localization and Tracking Using Stereo Vison System

Article Preview

Abstract:

The aim of this study was to design an moving object detection, localization and tracking algorithm able to detect, localize and track especially humans and vehicles. We focused on triangulation techniques to calculate the position of the detected objects in a stereo vision rig coordinates frame. For objects detection and tracking the novel algorithm, based on statistical image processing methods, was proposed. Verification of a proper operation of the elaborated method was made by conducting series of experiments. Our results indicate that the algorithm localizes, detects and tracks objects accurately for the most tested conditions.

You might also be interested in these eBooks

Info:

Periodical:

Solid State Phenomena (Volume 236)

Pages:

134-141

Citation:

Online since:

July 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] C. Micheloni, G. L. Foresti, A robust feature tracker for active surveillance of outdoor scenes, Electronic Letters on Computer Vision and Image Analysis (2003) 21-34.

DOI: 10.5565/rev/elcvia.60

Google Scholar

[2] R. Cucchiara, A. Prati, R. Vezzani R, Advanced video surveillance with pan tilt zoom cameras, Proc. of the 6th IEEE International Workshop on Visual Surveillance (2006) 334-352.

Google Scholar

[3] W. Hu, T. Tan, L. Wang, S. Maybank, A survey on visual surveillance of object motion and behaviors, IEEE Trans. on Systems, Man, and Cybernetics 34 (2004) 334-352.

DOI: 10.1109/tsmcc.2004.829274

Google Scholar

[4] I. Cohen, G. Medioni, Detecting and tracking moving objects for video surveillance, Proc. IEEE Computer Vision and Pattern Recognition (1999) 1-7.

DOI: 10.1109/cvpr.1999.784651

Google Scholar

[5] A. Czyżewski, G. Szwoch, P. Dalka, P. Szczuko, A. Ciarkowski, D. Ellwart, T. Merta, K. Łopatka, Ł. Kulasek, J. Wolski, Multi-Stage Video Analysis Framework, Video Surveillance (2011) 161-216.

DOI: 10.5772/16088

Google Scholar

[6] R. Jain, R. Kasturi, B. Schunck, Machine Vision, McGraw-Hill Inc., New York, (1995).

Google Scholar

[7] D. Zhang, G. Lu, An edge and color oriented optical flow estimation using block matching, Int. Conf. Signal Processing 2 (2000) 1026-1032.

DOI: 10.1109/icosp.2000.891703

Google Scholar

[8] C. Lien, Targets Tracking in the Crowd, Video Surveillance (2011) 232-246.

Google Scholar

[9] E.A. Ince, N.S. Naraghi, S.G. Ebrahimi, Background Subtraction and Lane Occupancy Analysis, Video Surveillance (2011) 175-199.

Google Scholar

[10] M. Li, J. Lavest, Some Aspects of Zoom-Lens Camera Calibration, IEEE Transactions on Pattern Analysis and Machine Intelligence (1995) 1105-1110.

DOI: 10.1109/34.544080

Google Scholar

[11] B. Żak, S. Hożyń, Distance Measurement Using a Stereo Vision System, Solid State Phenomena, Trans Tech Publications 196 (2013) 189-197.

DOI: 10.4028/www.scientific.net/ssp.196.189

Google Scholar

[12] B. Cyganek, P. Siebert, An Introduction to 3D Computer Vision Techniques and Algorithms, John Willey & Sons, Chippenham, (2009).

Google Scholar

[13] E. Trucco, A. Verri, Introductory Techniques for 3D Computer Vision. Prentice-Hall, New Jersey, (1998).

Google Scholar

[14] B. Sugandi, K. Hyoungseop, J.K. Tan, I. Seiji, A Block Matching Technique for Object Tracking Based on Peripheral Increment Sign Correlation Image, Object Tracking (2011) 1-21.

DOI: 10.1109/iccce.2008.4580579

Google Scholar

[15] B. Żak, S. Hożyń, Segmentation Algorithm Using Method of Edge Detection, Solid State Phenomena, Trans Tech Publications 196 (2013) 206-211.

DOI: 10.4028/www.scientific.net/ssp.196.206

Google Scholar