Reinforcement Learning for Efficient Detection Reinforcement Learning (RL) has been recently used to (1) replace classical detectors such as SSD and Faster-RCNN, (2) replace exhaustive box proposal techniques in two-stage detectors, and (3) find ROIs in very large images to run a detector on. Most of the methods proposed in this categories focus on learning sequential policies. Under category (1), [3, 29] proposed a top-down sequential object detection models trained with Q-learning algorithm. Most of the RL methods associated with object detection fall into category (2). For example, [16] recursively divides up an image in a top-down approach where the divisions are decided by the RL agent. The box proposals returned by the agent are then passed through Fast-RCNN.

We examine how to use emerging far-infrared imager ensembles to detect certain objects of interest (e.g., faces, hands, people and animals) in synchronized RGB video streams at very low power. We formulate the problem as one of selecting subsets of sensing elements (among many thousand possibilities) from the ensembles for tests. The subset selection problem is naturally adaptive and online: testing certain elements early can obviate the need for testing many others later, and selection policies must be updated at inference time. We pose the ensemble sensor selection problem as a structured extension of test-cost-sensitive classification, propose a principled suite of techniques to exploit ensemble structure to speed up processing and show how to re-estimate policies fast. We estimate reductions in power consumption of roughly 50x relative to even highly optimized implementations of face detection, a canonical object-detection problem. We also illustrate the benefits of adaptivity and online estimation.