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.