Proceedings of the 10th International Conference on Computer Vision Theory and Applications - (Volume 2) (VISIGRAPP 2015) pages:400-407
VISAPP edition:10 location:Berlin, Germany date:11-14 March 2015
Accurate object detection has been studied thoroughly over the years. Although these techniques have become very precise, they lack the capability to cope with a rotated appearance of the object. In this paper we tackle this problem in a two step approach. First we train a specific model for each orientation we want to cover. Next to that we propose the use of a rotation map that contains the predicted orientation information at a specific location based on the dominant orientation. This helps us to reduce the number of models that will be evaluated at each location. Based on 3 datasets, we obtain a high speed-up while still maintaining accurate rotated object detection.