Cascade-Dispatched Classifier Ensemble and Regressor for Pedestrian Detection

This paper focuses on ensemble classifiers for pedestrian detection. Ensemble learning is widely used in this field for context disambiguation or via a cascade-of-rejectors. However, applying the typical, parallel, instance of it remains disappointing in most cases. Our work studies the mechanisms that hinder the efficiency of ensemble classifiers for pedestrian detection, and, based on our findings, we introduce a structured classifier ensemble that improves performance without loss of speed. We also harness this principle for context disambiguation via the application of a regressor to pedestrian detection. Experiments on the INRIA and Caltech-USA datasets validate the approach.