Deep tracking in the wild: End-to-end tracking using recurrent neural networks
This paper presents a novel approach for tracking static and dynamic objects for an autonomous vehicle operating in complex urban environments. Whereas traditional approaches for tracking often feature numerous hand-engineered stages, this method is learned end-to-end and can directly predict a full...
| Main Authors: | , , , , |
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| 格式: | Journal article |
| 出版: |
SAGE Publications
2017
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| _version_ | 1826264309451194368 |
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| author | Dequaire, J Ondrúška, P Rao, D Wang, D Posner, H |
| author_facet | Dequaire, J Ondrúška, P Rao, D Wang, D Posner, H |
| author_sort | Dequaire, J |
| collection | OXFORD |
| description | This paper presents a novel approach for tracking static and dynamic objects for an autonomous vehicle operating in complex urban environments. Whereas traditional approaches for tracking often feature numerous hand-engineered stages, this method is learned end-to-end and can directly predict a fully unoccluded occupancy grid from raw laser input. We employ a recurrent neural network to capture the state and evolution of the environment, and train the model in an entirely unsupervised manner. In doing so, our use case compares to model-free, multi-object tracking although we do not explicitly perform the underlying data-association process. Further, we demonstrate that the underlying representation learned for the tracking task can be leveraged via inductive transfer to train an object detector in a data efficient manner. We motivate a number of architectural features and show the positive contribution of dilated convolutions, dynamic and static memory units to the task of tracking and classifying complex dynamic scenes through full occlusion. Our experimental results illustrate the ability of the model to track cars, buses, pedestrians, and cyclists from both moving and stationary platforms. Further, we compare and contrast the approach with a more traditional model-free multi-object tracking pipeline, demonstrating that it can more accurately predict future states of objects from current inputs. |
| first_indexed | 2024-03-06T20:05:43Z |
| format | Journal article |
| id | oxford-uuid:28d6b8af-8d7b-4dda-a36d-74e39b14a19f |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:05:43Z |
| publishDate | 2017 |
| publisher | SAGE Publications |
| record_format | dspace |
| spelling | oxford-uuid:28d6b8af-8d7b-4dda-a36d-74e39b14a19f2022-03-26T12:15:25ZDeep tracking in the wild: End-to-end tracking using recurrent neural networksJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:28d6b8af-8d7b-4dda-a36d-74e39b14a19fSymplectic Elements at OxfordSAGE Publications2017Dequaire, JOndrúška, PRao, DWang, DPosner, HThis paper presents a novel approach for tracking static and dynamic objects for an autonomous vehicle operating in complex urban environments. Whereas traditional approaches for tracking often feature numerous hand-engineered stages, this method is learned end-to-end and can directly predict a fully unoccluded occupancy grid from raw laser input. We employ a recurrent neural network to capture the state and evolution of the environment, and train the model in an entirely unsupervised manner. In doing so, our use case compares to model-free, multi-object tracking although we do not explicitly perform the underlying data-association process. Further, we demonstrate that the underlying representation learned for the tracking task can be leveraged via inductive transfer to train an object detector in a data efficient manner. We motivate a number of architectural features and show the positive contribution of dilated convolutions, dynamic and static memory units to the task of tracking and classifying complex dynamic scenes through full occlusion. Our experimental results illustrate the ability of the model to track cars, buses, pedestrians, and cyclists from both moving and stationary platforms. Further, we compare and contrast the approach with a more traditional model-free multi-object tracking pipeline, demonstrating that it can more accurately predict future states of objects from current inputs. |
| spellingShingle | Dequaire, J Ondrúška, P Rao, D Wang, D Posner, H Deep tracking in the wild: End-to-end tracking using recurrent neural networks |
| title | Deep tracking in the wild: End-to-end tracking using recurrent neural networks |
| title_full | Deep tracking in the wild: End-to-end tracking using recurrent neural networks |
| title_fullStr | Deep tracking in the wild: End-to-end tracking using recurrent neural networks |
| title_full_unstemmed | Deep tracking in the wild: End-to-end tracking using recurrent neural networks |
| title_short | Deep tracking in the wild: End-to-end tracking using recurrent neural networks |
| title_sort | deep tracking in the wild end to end tracking using recurrent neural networks |
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