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• W2963979855 abstract "We formulate tracking as an online decision-making process, where a tracking agent must follow an object despite ambiguous image frames and a limited computational bud- get. Crucially, the agent must decide where to look in the upcoming frames, when to reinitialize because it believes the target has been lost, and when to update its appearance model for the tracked object. Such decisions are typically made heuristically. Instead, we propose to learn an optimal decision-making policy by formulating tracking as a partially observable decision-making process (POMDP). We learn policies with deep reinforcement learning algorithms that need supervision (a reward signal) only when the track has gone awry. We demonstrate that sparse rewards al- low us to quickly train on massive datasets, several orders of magnitude more than past work. Interestingly, by treat- ing the data source of Internet videos as unlimited streams, we both learn and evaluate our trackers in a single, unified computational stream." @default.
• W2963979855 created "2019-07-30" @default.
• W2963979855 creator A5004353237 @default.
• W2963979855 creator A5086960085 @default.
• W2963979855 date "2017-10-01" @default.
• W2963979855 modified "2023-10-04" @default.
• W2963979855 title "Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning" @default.
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• W2963979855 doi "https://doi.org/10.1109/iccv.2017.43" @default.
• W2963979855 hasPublicationYear "2017" @default.
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