System and Algorithms on Detection of Objects Embedded in Perspective Geometry using Monocular Cameras

In this work, we present a framework to detect objects embedded in complex perspective geometry. Our goal is to accurately identify objects such as people standing in balconies or windows on building facades of surrounding buildings. Compared to traditional computer vision work focused on activity analysis from a horizontal view, our framework provides a solution for the application domain of mobile surveillance in urban areas. A novel solution for a monocular camera is formulated by tightly coupling various computational modules including geometric analysis, segmentation, scale estimation, and object detection. In particular, our proposed approach alleviates the effect of the perspective geometry and corresponding distortion in object appearance effectively, and provides accurate scale priors to eliminate unlikely object detection hypotheses. The experimental results on collected video dataset show that the proposed approach is more accurate than traditional detection approaches based on brute-force scanning windows.