Near Real-time Object Detection in RGBD Data

Abstract

Most methods of object detection with RGBD cameras set hard constraints on their operational area. They only work with specific objects, in specific environments, or rely on time consuming computations. In the context of home robotics, such hard constraints cannot be made. Specifically, an autonomous home robot shall be equipped with an object detection pipeline that runs in near real-time and produces reliable results without restricting object type and environment. For this purpose, a baseline framework that works on RGB data only is extended by suitable depth features that are selected on the basis of a comparative evaluation. The additional depth data is further exploited to reduce the computational cost of the detection algorithm. A final evaluation of the enhanced framework shows significant improvements compared to its original version and state-of-the-art methods in terms of both, detection performance and real-time capability.