Wavelet-Based Data Reduction for Detection of Moving Objects

• Autorzy: Pamula W.
• Języki publikacji: EN

Abstrakty

EN

The detection of moving objects in video streams is generally performed by analysis of the differences between the modelled background and the current stream content, by matching object models, extracting and clustering the features of objects or else by using various filtering methods. Filtering is performed on the transformed contents of the video stream. Due to implementational constraints, mainly limited processing resources, solutions based on these principles of detection are sensitive to ambient light variations, objects shadows and camera movement. This paper presents a method for the detection of moving objects that uses a data reduction technique based on wavelets. Instead of the analysis of raw video data, wavelet coefficients of an appropriate scale are explored. In order to satisfy low processing requirements, an integer version of discrete wavelet transform is chosen for processing. To facilitate the detection, each frame is converted into a vector of pixel values. Consecutive video vectors are transformed using one-dimensional Discrete Wave Transform (DWT). The computed DWT coefficients make up a surface, which maps changes in their values over time. The surface is analysed to find clusters of values corresponding to moving objects. The checked patches represent moving objects. The width of a patch indicates the object size. Background details and illumination changes are represented by gradually changing patterns. Various examples demonstrate the potential of the method for practical applications.

Słowa kluczowe

EN

DWT; analysis of temporal changes; moving objects; video stream

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2011
• Tom: Vol. 20, No. 1
• Strony: 27--40
• Opis fizyczny: Bibliogr. 17 poz.

Twórcy

autor

Pamula W.

• Faculty of Transport, Silesian University of Technology, Krasinskiego 8, Katowice, Poland

Bibliografia

• 1. G. Strang, Wavelets and dilation equations: a brief introduction, SIAM Review, v. 31 n. 4, pp. 614-627, Dec. 1989.
• 2. Calderbank A. R., Daubechies I., Sweldens W.: Wavelet Transforms that Map Integers to Integers, Applied and computational harmonic analysis 5, (1998) pp. 332-369.
• 3. Chris Stauffer, W. Eric, L. Grimson, Learning Patterns of Activity Using Real-Time Tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 22 n. 8, pp. 747-757, August 2000.
• 4. Bongki Moon, H. v. Jagadish, Christos Faloutsos, Joel H. Saltz, Analysis of the Clustering Properties of the Hilbert Space-Filling Curve, IEEE Transactions on Knowledge and Data Engineering, v. 13 n. 1, pp. 124-141, January 2001.
• 5. Wieslaw Pamula, Advantages of Using a Space Filling Curve for Computing Wavelet Transforms of Road Traffic Images, Proceedings of the 12th International Conference on Image Analysis and Processing, pp. 221-227, Mantova, September 17-19, 2003.
• 6. Li, L., Huang, W., Gu, I.Y.-H., Tian, Q. : Statistical Modeling of Complex Backgrounds for Foreground Object Detection, IEEE Trans. on Image Processing, no 13 vol 11, (2004) pp. 1459-1472.
• 7. Piccardi, M.: Background subtraction techniques: a review, Proc. of SMC vol. 4. (2004), pp. 3099-3104.
• 8. Toreyin B. U., Cetin A. E., Anil Aksaya A., Akhan M. B.:Moving object detection in wavelet compressed video, Signal Processing: Image Communication 20 (2005), pp. 255-264.
• 9. Y. Andreopoulos, A. Munteanu, G. Van der Auwera, J. P. H. Cornelis, P. Schelkens, Complete-to-overcomplete discrete wavelet transforms: theory and applications, IEEE Transactions on Signal Processing, v. 53 n. 4, pp. 1398-1412, April 2005.
• 10. Mustafa Oral, Umut Deniz, Centre of mass model - A novel approach to background modelling for segmentation of moving objects, Image and Vision Computing, v. 25 n. 8, pp. 1365-1376, August, 2007.
• 11. Antoine Manzanera, Julien C. Richefeu, A new motion detection algorithm based on Σ-Λ background estimation, Pattern Recognition Letters, v. 28 n. 3, pp. 320-328, February, 2007.
• 12. Jing Li, Nigel M. Allinson, A comprehensive review of current local features for computer vision, Neurocomputing, v. 71 n. 10-12, pp. 1771-1787, June, 2008.
• 13. Du-Ming Tsai, Wei-Yao Chiu, Motion detection using Fourier image reconstruction, Pattern Recognition Letters, v. 29 n. 16, pp. 2145-2155, December, 2008.
• 14. Jing Li, Nigel M. Allinson, A comprehensive review of current local features for computer vision, Neurocomputing, v. 71 n. 10-12, pp. 1771-1787, June, 2008.
• 15. Pamula W.: Vehicle Detection Algorithm for FPCA Based Implementation, in Computer Recognition Systems Eds. M. Kurzyfiski, M. Wozniak, Springer Verlag Berlin (2009) pp. 586-592.
• 16. Huiyu Zhou, Yuan Yuan, Chunmei Shi, Object tracking using SIFT features and mean shift, Computer Vision and Image Understanding, v. 113 n. 3, pp. 345-352, March, 2009.
• 17. Jeong Y-S., Castro-Neto M., Jeong M. K., Han L. D.: A wavelet-based freeway incident detection algorithm with adapting threshold parameters, Transportation Research Part C 19 (2011) pp. 1-19.