Object Detection Using a Cascade of 3D Models

We present an alignment framework for object detection using a hierarchy of 3D polygonal models. One difficulty with alignment methods is that the high-dimensional transformation space makes finding potential candidate states a time-consuming task. This is an important consideration in our approach, as an exhaustive search is applied on a densely-sampled state space in order to avoid local minima and to extract all possible candidates. In our framework, a level-of-detail (LOD) 3D geometric model hierarchy is generated for the target object. Each of this model acts as a classifier to determine which of the discrete states are potential candidates. The classification is done through the estimation of pixel and edge-based mutual information between the 3D model and the image, where the classification speed significantly depends on the LOD and resolution of the image. By combining these models of various LOD into a cascade, we show that search time can be reduced significantly while accuracy is maintained.