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An object detection algorithm which can remove moving cast shadows is presented. It is based on the homography mapping of foreground regions from multiple cameras. Not only the homography for the ground plane but also those for multiple parallel planes are employed. Unlike the existing geometric approaches, this algorithm removes cast shadows while keeping the feet of moving objects.

An object detection algorithm by using multiple cameras is proposed. The information fusion is based on homography mapping of the foreground information from multiple cameras and for multiple parallel planes. Unlike the most recent algorithms which transmit and project foreground bitmaps, it approximates each foreground silhouette with a polygon and projects the polygon vertices only. In addition, an alternative approach to estimating the homographies for multiple parallel planes is presented. It is based on the observed pedestrians and does not resort to vanishing point estimation. The ability of this algorithm to remove cast shadows in moving object detection is also investigated. The results on open video datasets are demonstrated.

In this paper an object detection algorithm is proposed, which is robust in the presence of cast shadows and is based on geometric projections. The novelty of the work lies in the use of homology mapping of the foreground regions between different parallel planes within a monocular view, unlike some existing algorithms which depend on the use of multiple cameras. The results on an open video dataset are provided.

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