Boston University, Boston,MA 02215,USA.
IEEE Trans Image Process. 2010 Oct;19(10):2595-613. doi: 10.1109/TIP.2010.2052824. Epub 2010 Jun 14.
In this paper, we consider the problem of finding correspondences between distributed cameras that have partially overlapping field of views. When multiple cameras with adaptable orientations and zooms are deployed, as in many wide area surveillance applications, identifying correspondence between different activities becomes a fundamental issue. We propose a correspondence method based upon activity features that, unlike photometric features, have certain geometry independence properties. The proposed method is robust to pose, illumination and geometric effects, unsupervised (does not require any calibration objects). In addition, these features are amenable to low communication bandwidth and distributed network applications. We present quantitative and qualitative results with synthetic and real life examples, and compare the proposed method with scale invariant feature transform (SIFT) based method. We show that our method significantly outperforms the SIFT method when cameras have significantly different orientations. We then describe extensions of our method in a number of directions including topology reconstruction, camera calibration, and distributed anomaly detection.
在本文中，我们考虑了在具有部分重叠视场的分布式摄像机之间找到对应关系的问题。当在许多广域监控应用中部署具有可适应方向和缩放的多个摄像机时，识别不同活动之间的对应关系成为一个基本问题。我们提出了一种基于活动特征的对应方法，与光度特征不同，它具有一定的几何独立性。所提出的方法对姿势、光照和几何效果具有鲁棒性，是无监督的（不需要任何校准对象）。此外，这些特征适用于低通信带宽和分布式网络应用。我们通过合成和真实生活示例展示了定量和定性结果，并将提出的方法与尺度不变特征变换（SIFT）方法进行了比较。我们表明，当摄像机具有显著不同的方向时，我们的方法明显优于 SIFT 方法。然后，我们描述了该方法在多个方向上的扩展，包括拓扑重建、摄像机校准和分布式异常检测。
