Context-Sensitive Decision Forests for Object Detection - Kontschieder, Peter; Bulo, Samuel Rota; Criminisi, Antonio; Kohli, Pushmeet; Pelillo, Marcello; Bischof, Horst (NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012)
In this paper we introduce Context-Sensitive Decision Forests - A new perspective to exploit contextual information in the popular decision forest framework for the object detection problem. They are tree-structured classiﬁers with the ability to access intermediate prediction (here: classiﬁcation and regression) information during training and inference time. This intermediate prediction is available for each sample and allows us to develop context-based decision criteria, used for reﬁning the prediction process. In addition, we introduce a novel split criterion which in combination with a priority based way of constructing the trees, allows more accurate regression mode selection and hence improves the current context information. In our experiments, we demonstrate improved results for the task of pedestrian detection on the challenging TUD data set when compared to state-of-the-art methods