Real-time detection of multiple moving objects in complex image sequences

In this article, a real-time multistage method for detecting multiple objects moving in real scenes is presented. At the first level, a rough focus-of-attention mechanism is used to individuate areas of the input image that show remarkable differences with a real-time updated background image. Binary statistical morphology (BMS) operators are applied to individuate image pixels, which can be associated with real objects moving into the scene. High stability to noise is obtained by tuning the smoothing effects of the BSM operators according to the noise level present in the original image sequence. Then, at the second level, a composition of BSM is applied to eliminate isolated points and to favor dense agglomerate of changed pixels, i.e., blobs. The last level attempts to describe changes in terms of motion of blobs by allowing blobs to merge, split, appear, and vanish. A blob-matching procedure is used for tracking blobs over consecutive frames. Experimental results on real scenes, which demonstrate the advantages of the proposed method with respect to existing change detection methods, are given. (C) 1999 John Wiley & Sons, Inc.