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• W2894909756 abstract "In this paper, we propose a collaborative deep reinforcement learning (C-DRL) method for multi-object tracking. Most existing multi-object tracking methods employ the tracking-by-detection strategy which first detects objects in each frame and then associates them across different frames. However, the performance of these methods rely heavily on the detection results, which are usually unsatisfied in many real applications, especially in crowded scenes. To address this, we develop a deep prediction-decision network in our C-DRL, which simultaneously detects and predicts objects under a unified network via deep reinforcement learning. Specifically, we consider each object as an agent and track it via the prediction network, and seek the optimal tracked results by exploiting the collaborative interactions of different agents and environments via the decision network. Experimental results on the challenging MOT15 and MOT16 benchmarks are presented to show the effectiveness of our approach." @default.
• W2894909756 created "2018-10-12" @default.
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• W2894909756 date "2018-01-01" @default.
• W2894909756 modified "2023-10-02" @default.
• W2894909756 title "Collaborative Deep Reinforcement Learning for Multi-object Tracking" @default.
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• W2894909756 doi "https://doi.org/10.1007/978-3-030-01219-9_36" @default.
• W2894909756 hasPublicationYear "2018" @default.
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