Localization of detected objects in multi-camera network

In this paper we present a framework for detecting, recognizing, and localizing objects in overlapping multi-camera network. The three main components of the framework include background change detection, object recognition, and object localization. The background change detection is based on analyzing wavelet transform coefficients of small patches of non-overlapping 3D texture maps. Detected changed background becomes the region of interest which is scanned to recognize various objects of interest. The object recognition is based on model histogram ratios of gradient magnitude patches. The supervised learning of objects is performed by a support vector machine. A homographic spatial transformation brings multiple cameras into alignment with the ground plane to localize objects in 2D space. Experimental results are demonstrated using various benchmark video sequences and object category datasets.