A PCA-Based Technique to Detect Moving Objects
Moving objects detection is a crucial step for video surveillance systems. The segmentation performed by motion detection algorithms is often noisy, which makes it hard to distinguish between relevant motion and noise motion. This article describes a new approach to make such a distinction using principal component analysis (PCA), a technique not commonly used in this domain. We consider a ten-frame subsequence, where each frame is associated with one dimension of the feature space, and we apply PCA to map data in a lower-dimensional space where points picturing coherent motion are close to each other. Frames are then split into blocks that we project in this new space. Inertia ellipsoids of the projected blocks allow us to qualify the motion occurring within the blocks. The results obtained are encouraging since we get very few false positives and a satisfying number of connected components in comparison to other tested algorithms.
KeywordsData analysis motion detection principal component analysis video sequence analysis video surveillance
- 1.Toyama, K., Krumm, J., Brummit, B., Meyers, B.: Wallflower: Principles and practice of background maintenance. In: Proc. IEEE Int. Conf. on Computer Vision (ICCV’99), vol. 1, Kerkyra, Corfu, Greece, Sept. 1999, pp. 255–261. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
- 7.Koller, D., Weber, J., Malik, J.: Robust multiple car tracking with occlusion reasoning. Technical Report UCB/CSD-93-780, University of California at Berkeley, EECS Department, Berkeley, CA (1993)Google Scholar
- 8.Ma, Y.-F., Zhang, H.-J.: Detecting motion object by spatio-temporal entropy. In: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2001), Tokyo, Japan, pp. 265–268. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
- 9.Guo, J., Chng, E.S., Rajan, D.: Foreground motion detection by difference-based spatial temporal entropy image. In: Proc. IEEE Region 10 Conf (TenCon 2004), Chiang Mai, Thailand, pp. 379–382. IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
- 10.Fodor, I.K.: A survey of dimension reduction techniques. Report UCRL-ID-148494, Lawrence Livermore National Laboratory, Livermore, CA (2002)Google Scholar