Beyond straight lines — Object detection using curvature

We present an approach that directly uses curvature cues in a discriminative way to perform object recognition. We show that integrating curvature information substantially improves detection results over descriptors that solely rely upon histograms of orientated gradients (HoG). The proposed approach is generic in that it can be easily integrated into state-of-the-art object detection systems. Results on two challenging datasets are presented: ETHZ Shape Dataset and INRIA horses Dataset, improving state-of the-art results using HoG by 7.6% and 12.3% in average precision (AP), respectively. In particular, we achieve higher recall at lower false positive rates.

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