Detecting Anomalous Objects on Mobile Platforms

Abstract

We present an approach where a robot patrols a fixed path through an environment, autonomously locating suspicious or anomalous objects. To learn, the robot patrols this environment building a dictionary describing what is present. The dictionary is built by clustering features from a deep neural network. The objects present vary depending on the scene, which means that an object that is anomalous in one scene may be completely normal in another. To reason about this, the robot uses a computational cognitive model to learn the dictionary elements that are typically found in each scene. Once the dictionary and model has been built, the robot can patrol the environment matching objects against the dictionary, and querying the model to find the most likely objects present and to determine which objects (if any) are anomalous. We demonstrate our approach by patrolling two indoor and one outdoor environments.