Combining Geometry and Local Appearance for Object Detection

In this paper we address the problem of object detection in cluttered scenes. Local image features and their spatial configuration act as representation of object classes which are learned in a discriminative fashion. Recent contributions in the area of object detection indicate the importance of using geometrical properties for representing object classes. Prompted by this, we devised an approach tailored to control the importance of the features and their spatial alignment. We quantitatively show that modeling the spatial distribution of local features and optimising the influence of both cues significantly boosts object detection performance.