Shufflets: Shared Mid-level Parts for Fast Object Detection

We present a method to identify and exploit structures that are shared across different object categories, by using sparse coding to learn a shared basis for the 'part' and 'root' templates of Deformable Part Models (DPMs). Our first contribution consists in using Shift-Invariant Sparse Coding (SISC) to learn mid-level elements that can translate during coding. This results in systematically better approximations than those attained using standard sparse coding. To emphasize that the learned mid-level structures are shiftable we call them shufflets. Our second contribution consists in using the resulting score to construct probabilistic upper bounds to the exact template scores, instead of taking them 'at face value' as is common in current works. We integrate shufflets in DualTree Branch-and-Bound and cascade-DPMs and demonstrate that we can achieve a substantial acceleration, with practically no loss in performance.