Ensembles of Correlation Filters for Object Detection

Traditional correlation filters for object detection are efficient and provide good localization, but require scalar valued image features and only perform well on objects with consistent appearance. Some newer filters work with feature spaces that introduce some invariance to small deformations, but more difficult detection problems require more than one filter. We introduce a method for jointly learning an ensemble of correlation filters that collectively capture as much variation in object appearance as possible. During training our filters adapt to the needs of the training data with no restrictions on size or scope. We demonstrate performance that exceeds the state of the art in several challenging experiments.