Top-down Spatial Attention for Visual Search: Novelty Detection-Tracking Using Spatial Memory with a Mobile Robot

Abstract

Assistive robotics technologies have been growing impact on at-home monitoring services to support daily life. One of the main research fields is to develop an autonomous mobile robot with the tasks detection, tracking, observation and analysis of the subject of interest in the indoor environment. The main challenges in such daily monitoring application, thus in visual search, are that the robot should track the subject successfully in several severe varying conditions. Recent color and depth image based visual search methods can help to handle part of the problems, such as changing illumination, occlusion, and etc. but these methods generally use large amount of training data by checking the whole scene with high redundancy to find the region of interest. Therefore, inspired by the idea that spatial memory can reveal novelty regions for finding the attention points as in Human Visual System (HVS), we proposed a simple and novel algorithm that integrates Kinect and Lidar(Light Detection And Ranging) sensor data to detect and track novelties using the environment map of the robot as a top-down approach without the necessity of large amount of training data. Then, novelty detection and tracking is achieved based on space based saliency map representing the novelty on the scene. Experimental results demonstrated that the proposed visual attention based scene analysis can handle various conditions stated and achieve high accuracy of novelty detection and tracking.