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An Empirical Study of Deformable Part Model with fast feature pyramid

The performance of an object detection system relies heavily on two components: an object model to capture the compositional relationship among the object body and its parts, and a feature representation to describe object appearance. In this work, we present an empirical study of combining two state-of-the-art such components: Deformable Part Model (DPM), a proven effective and flexible part-based object model which originally adopts Histogram of Oriented Gradients (HOG) feature, and Aggregated Channel Features (ACF), a unified feature representation framework with fast pyramid calculation which is originally used in a rigid template matching scheme. DPM is known to work but slow, at the same time ACF has previously been shown to yield a massive speedup with only a minor loss in accuracy compared to competing features including HOG. By combining the two, our hope is to achieve the best of both worlds: the object structure representation power of DPM and the computational efficiency of ACF. Our experiments show that while ACF with heterogeneous feature channels could improve the accuracy of DPM, the run time benefit introduced by fast pyramid approximation is rather limited.