場所とは何か？ロボティクス向けの場所特化型物体検出器の構築
What Makes a Place? Building Bespoke Place Dependent Object Detectors for Robotics
ロボットが移動する場所ごとに物体検出器を適応させ、その場所に特化した軽量な歩行者検出モデルを構築することで、汎用最先端検出器を大きく上回る性能を実現した。
著者: Jeffrey Hawke, Alex Bewley, Ingmar Posner
分類: cs.RO, cs.CV
原文アブストラクト
This paper is about enabling robots to improve their perceptual performance through repeated use in their operating environment, creating local expert detectors fitted to the places through which a robot moves. We leverage the concept of 'experiences' in visual perception for robotics, accounting for bias in the data a robot sees by fitting object detector models to a particular place. The key question we seek to answer in this paper is simply: how do we define a place? We build bespoke pedestrian detector models for autonomous driving, highlighting the necessary trade off between generalisation and model capacity as we vary the extent of the place we fit to. We demonstrate a sizeable performance gain over a current state-of-the-art detector when using computationally lightweight bespoke place-fitted detector models.
