A metric on the space of finite sets of trajectories for evaluation of multi-target tracking algorithms
This provides a rigorous evaluation framework for researchers and practitioners in multi-target tracking, addressing a known bottleneck in the field, though it is incremental as it builds on existing metric concepts.
The paper tackles the problem of evaluating multi-target tracking algorithms by proposing a mathematically sound metric on the space of finite sets of trajectories, which includes costs for localization error, missed/false targets, and track switches, with computation based on solving a multi-dimensional assignment problem and a polynomial-time lower bound using linear programming.
In this paper, we propose a metric on the space of finite sets of trajectories for assessing multi-target tracking algorithms in a mathematically sound way. The main use of the metric is to compare estimates of trajectories from different algorithms with the ground truth of trajectories. The proposed metric includes intuitive costs associated to localization error for properly detected targets, missed and false targets and track switches at each time step. The metric computation is based on solving a multi-dimensional assignment problem. We also propose a lower bound for the metric, which is also a metric for sets of trajectories and is computable in polynomial time using linear programming. We also extend the proposed metrics on sets of trajectories to random finite sets of trajectories.