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Localization of detected objects in multi-camera network

机译：多相机网络中检测到的对象的本地化

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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.
机译：在本文中，我们介绍了一种用于检测，识别和本地化对象中的重叠多摄像机网络的框架。该框架的三个主要组件包括背景改变检测，对象识别和对象本地化。背景改变检测基于分析非重叠3D纹理映射小斑块的小波变换系数。检测到的改变背景成为扫描的感兴趣区域以识别各种感兴趣的对象。对象识别基于梯度幅度块的模型直方图比率。对对象的监督学习由支持向量机执行。相同的空间转换将多个摄像机带入与地面平面对齐，以将物体定位在2D空间中。使用各种基准视频序列和对象类别数据集来证明实验结果。

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