Existing performance measures such as CLEAR MOT report how often a tracker makes what types of incorrect decisions. We argue that some system users may instead be more interested in how well they can determine who is where at all times.

Motivation

• CLEAR Metrcs: Event-based
  • can help to pinpoint diffenrent types of errors.
• Identity-based measures
  • how well computed identities conform to true identities
  • disregarding where or why mistakes occur
    • 

Our measures apply both within and across cameras.

Once that choice is made, every frame in which A is assigned to the wrong computed identity is a frame in which the tracker is in error.

In some scenarios like sports, security, or surveillance, preserving identity is crucial.

We should consider the coverage ratio of consistent ID over all frames, instead of the consistency itself at certain frames where changes occur, e.g. the fragmentations.

In this paper, it also mentioned Handover

We want to focus on Id-based metrics here. Refer to the paper if you're interested.

Identity-based measures

we propose to measure performance not by how often mismatches occur, but by how long the tracker correctly identities targets.

[Note

标签：Multi,gt,IDF1,ids,num,hyp,Set,tracker,id
来源： https://www.cnblogs.com/zxyfrank/p/16155336.html