リカレントニューラルネットワークによる追跡と意味セグメンテーションのエンドツーエンド学習
End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks
生のレーザー計測列をRNNで直接処理し、物体位置と種類を遮蔽領域も含めて推定する枠組みを提案。教師なし追跡で事前学習し、少量のラベル付きデータで意味分類を転移学習する。
著者: Peter Ondruska, Julie Dequaire, Dominic Zeng Wang, Ingmar Posner
分類: cs.LG, cs.AI, cs.CV, cs.NE, cs.RO
原文アブストラクト
In this work we present a novel end-to-end framework for tracking and classifying a robot's surroundings in complex, dynamic and only partially observable real-world environments. The approach deploys a recurrent neural network to filter an input stream of raw laser measurements in order to directly infer object locations, along with their identity in both visible and occluded areas. To achieve this we first train the network using unsupervised Deep Tracking, a recently proposed theoretical framework for end-to-end space occupancy prediction. We show that by learning to track on a large amount of unsupervised data, the network creates a rich internal representation of its environment which we in turn exploit through the principle of inductive transfer of knowledge to perform the task of it's semantic classification. As a result, we show that only a small amount of labelled data suffices to steer the network towards mastering this additional task. Furthermore we propose a novel recurrent neural network architecture specifically tailored to tracking and semantic classification in real-world robotics applications. We demonstrate the tracking and classification performance of the method on real-world data collected at a busy road junction. Our evaluation shows that the proposed end-to-end framework compares favourably to a state-of-the-art, model-free tracking solution and that it outperforms a conventional one-shot training scheme for semantic classification.
