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Context-Sensitive Decision Forests for Object Detection
Peter Kontschieder · Samuel Rota Bulò · Antonio Criminisi · Pushmeet Kohli · Marcello Pelillo · Horst Bischof

Wed Dec 05 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

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 to each sample, which 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.

Author Information

Peter Kontschieder (Graz University of Technology)
Samuel Rota Bulò (Università Ca' Foscari Venezia, Italy)
Antonio Criminisi (Microsoft Research)
Pushmeet Kohli (Microsoft Research)
Marcello Pelillo (Università Ca' Foscari di Venezia)
Horst Bischof (TU Graz)

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