  Active Multi-Object Tracking (AMOT) is a task where cameras are controlled by
a centralized system to adjust their poses automatically and collaboratively so
as to maximize the coverage of targets in their shared visual field. In AMOT,
each camera only receives partial information from its observation, which may
mislead cameras to take locally optimal action. Besides, the global goal, i.e.,
maximum coverage of objects, is hard to be directly optimized. To address the
above issues, we propose a coordinate-aligned multi-camera collaboration system
for AMOT. In our approach, we regard each camera as an agent and address AMOT
with a multi-agent reinforcement learning solution. To represent the
observation of each agent, we first identify the targets in the camera view
with an image detector, and then align the coordinates of the targets in 3D
environment. We define the reward of each agent based on both global coverage
as well as four individual reward terms. The action policy of the agents is
derived with a value-based Q-network. To the best of our knowledge, we are the
first to study the AMOT task. To train and evaluate the efficacy of our system,
we build a virtual yet credible 3D environment, named "Soccer Court", to mimic
the real-world AMOT scenario. The experimental results show that our system
achieves a coverage of 71.88%, outperforming the baseline method by 8.9%.
