Parallelized deformable part models with effective hypothesis pruning

As a typical machine-learning based detection technique, deformable part models (DPM) achieve great success in detecting complex object categories. The heavy computational burden of DPM, however, severely restricts their utilization in many real world applications. In this work, we accelerate DPM via parallelization and hypothesis pruning. Firstly, we implement the original DPM approach on a GPU platform and parallelize it, making it 136 times faster than DPM release 5 without loss of detection accuracy. Furthermore, we use a mixture root template as a pre-filter for hypothesis pruning, and achieve more than 200 times speedup over DPM release 5, apparently the fastest implementation of DPM yet. The performance of our method has been validated on the Pascal VOC 2007 and INRIA pedestrian datasets, and compared to other state-of-the-art techniques.