Metrics on the space of sets of trajectories are important for scientists in the field of computer vision, machine learning, robotics, and general artificial intelligence. However, existing notions of closeness between sets of trajectories are either mathematically inconsistent or of limited practical use. In this paper, we outline the limitations in the current mathematically-consistent metrics, which are based on OSPA ( 2008); and the inconsistencies in the heuristic notions of closeness used in practice, whose main ideas are common to the CLEAR MOT measures (Keni and Rainer 2008) widely used in computer vision. In two steps, we then propose a new intuitive metric between sets of trajectories and address these limitations. First, we explain a solution that leads to a metric that is hard to compute. Then we modify this formulation to obtain a metric that is easy to compute while keeping the useful properties of the previous metric. Our notion of closeness is the first demonstrating the following three features: the metric 1) can be quickly computed, 2) incorporates confusion of trajectories' identity in an optimal way, and 3) is a metric in the mathematical sense.
The authors in @cite_34 define the OSPAMT to be a metric and to be more reliable than OSPA-T when we evaluate the performance of multi-target tracking algorithms. The OSPAMT metric also computes an optimal match between full trajectories in @math and full trajectories in @math but unlike OSPA-T allows to match one full trajectory in @math to multiple full trajectories in @math (and vice-versa). The authors make this design choice not to penalize a tracker when it outputs only one track for two objects that move closely together.
@cite_34
1881351112
This paper proposes a new metric based on the Optimal Sub-pattern Assignment (OSPA) metric for evaluating the performance of multiple target tracking algorithms. It is shown in this paper that by considering the properties of false tracks, missing tracks and miss-detections, the minimization of all distances between tracks enables performance evaluation of multi-target algorithms in a more comprehensive manner than is possible with the OSPA metric for track (OSPAT) introduced by