Deep Reinforcement Learning with Parameterized Action Space for Object Detection

Abstract

Object detection is a fundamental task in computer vision. With the remarkable progress made in big visual data analytics and deep learning, Reinforcement Learning (RL) is becoming a promising framework to model the object detection problem since the detection procedure can be cast as a Markov decision process (MDP). We propose a Reinforcement Learning system with parameterized action space for image object detection. The proposed system uses an active agent exploring in a scene to identify the location of a target object, and learns a policy to refine the geometry of the agent by taking simple actions in parameterized space, which integrates the discrete actions and its corresponding continuous parameters. We then optimize the representation of the generated region proposals with the discriminative multiple canonical correlation analysis (DMCCA) [11] in preparation for classification with Fast R-CNN. Experiments on PASCAL VOC 2007 and 2012 datasets show the effectiveness of the proposed method.