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Collaborative Deep Reinforcement Learning for Multi-Object Tracking

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, so that the influences of occlusions and noisy detection results can be well alleviated. Experimental results on the challenging MOT15 and MOT16 benchmarks are presented to show the efficiency of our approach.

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