End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks

TL;DR

This paper introduces an end-to-end recurrent neural network framework for real-time tracking and semantic classification of objects in complex environments, leveraging unsupervised learning and transfer of knowledge.

Contribution

The work presents a novel RNN architecture trained with unsupervised deep tracking, enabling effective environment understanding with minimal labeled data in real-world robotics.

Findings

Outperforms state-of-the-art model-free tracking methods

Requires less labeled data for semantic classification

Demonstrates effective real-world application at a busy road junction

Abstract

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.