Multi-View Object Detection by Classifier Interpolation

In this paper, we propose a novel solution for multi-view object detection. Given a set of training examples at different views, we select examples at a few key views and train one classifier for each of them. Then classifiers for more intermediate views can be interpolated from key views. The interpolation is conducted on the weights and positions of features, under the assumption that they can all be expressed as functions of view angle. Finally, the learned and interpolated classifiers are combined into a boosting framework to construct a multi-view classifier to further validate the effectiveness of the interpolation. Experiments of interpolated single view classifier and combined multi-view classifier are conducted on car data sets and their performances are compared to corresponding learned classifiers. The results illustrate that the interpolated classifiers give comparable performance as classifiers learned from data, and that the combined classifiers give similar results as their learned counterparts.