FLEXIBLE 3D NEIGHBORHOOD CASCADE DEFORMABLE PART MODELS FOR OBJECT DETECTION

Cascade Deformable Part Models (DPMs) are cascade frameworks to speed up Deformable Part Models (DPMs), which are one of the state-of-the-art solutions for object detection. Its idea is to reject most non-object hypotheses from the early stages of detection process. By investigating the dependency between hypotheses over scales, we introduce a novel pruning method to accelerate Cascade DPM frameworks. Our scale pruning method includes two following strategies: a) rejecting the neighboring scales if the score at a certain scale is too low; b) keeping a few scales with the highest scores, instead of storing all scales per location. Next, we extend this pruning technique to the 3D neighborhood pruning and describe a novel approach to evaluate the root score efficiently without full computation as existing cascade DPM methods. Finally, look-up tables are introduced to work with flexible neighborhood whose volume varies over hypotheses. As a result, our cascade model is equipped with an efficient and aggressive pruning mechanism. Extensive experiments reveal that the proposed method is faster than the state-of-the-art methods for both problems of object detection and face detection.