End-to-end tracking and semantic segmentation using recurrent neural networks
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...
| Main Authors: | , , , |
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| Format: | Conference item |
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Robotics: Science and Systems
2016
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| _version_ | 1797098429241884672 |
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| author | Ondruska, P Dequaire, J Zen Wang, D Posner, H |
| author_facet | Ondruska, P Dequaire, J Zen Wang, D Posner, H |
| author_sort | Ondruska, P |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T05:09:25Z |
| format | Conference item |
| id | oxford-uuid:db0b323e-676b-4e6a-98ce-1da54ffc2845 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T05:09:25Z |
| publishDate | 2016 |
| publisher | Robotics: Science and Systems |
| record_format | dspace |
| spelling | oxford-uuid:db0b323e-676b-4e6a-98ce-1da54ffc28452022-03-27T09:07:39ZEnd-to-end tracking and semantic segmentation using recurrent neural networksConference itemhttp://purl.org/coar/resource_type/c_5794uuid:db0b323e-676b-4e6a-98ce-1da54ffc2845Symplectic Elements at OxfordRobotics: Science and Systems2016Ondruska, PDequaire, JZen Wang, DPosner, HIn 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. |
| spellingShingle | Ondruska, P Dequaire, J Zen Wang, D Posner, H End-to-end tracking and semantic segmentation using recurrent neural networks |
| title | End-to-end tracking and semantic segmentation using recurrent neural networks |
| title_full | End-to-end tracking and semantic segmentation using recurrent neural networks |
| title_fullStr | End-to-end tracking and semantic segmentation using recurrent neural networks |
| title_full_unstemmed | End-to-end tracking and semantic segmentation using recurrent neural networks |
| title_short | End-to-end tracking and semantic segmentation using recurrent neural networks |
| title_sort | end to end tracking and semantic segmentation using recurrent neural networks |
| work_keys_str_mv | AT ondruskap endtoendtrackingandsemanticsegmentationusingrecurrentneuralnetworks AT dequairej endtoendtrackingandsemanticsegmentationusingrecurrentneuralnetworks AT zenwangd endtoendtrackingandsemanticsegmentationusingrecurrentneuralnetworks AT posnerh endtoendtrackingandsemanticsegmentationusingrecurrentneuralnetworks |