Bayesian Reinforcement Learning for Multiscale Combinatorial Grouping

Currently, most of the top performing object detectors apply proposal methods to guide the search for objects, in order to avoiding exhaustive sliding window search. As a classical proposal method, Multiscale Combinatorial Grouping (MCG) [1, 2] performs well on the PASCAL VOC dataset, especially for low proposal number. But when it comes to the autonomous driving object scenarios, the result is poor. In our paper, we applied Bayesian model to the proposals generated by MCG [1, 2] to re-rank the candidate bounding boxes using several geometrical features. We evaluated our method on the challenging KITTI dataset, the results shows that the Bayesian model can greatly improve the performance of MCG [1, 2] for better object detection.