Evaluation Metrics for Multiple Object Tracking

Learn different Metrics for evaluating the performance of a Multiple Object Tracking Algorithm

Multiple Object Tracking(MOT) is the task of detecting various objects of interest in a video, tracking these detected objects in subsequent frames by assigning them a unique ID, and maintaining these unique IDs as the objects move around in a video in successive frames.

Multiple Object Tracking(MOT) finds its application in video surveillance, robotics, or self-driving vehicles.

To understand the different metrics used to evaluate MOT algorithms, you first need to understand how MOT works.

MOT takes a single continuous video as an input and splits it into discrete frames at a specific frame rate(fps). The output of MOT is

• Detection: what objects are present in each frame
• Localization: where objects are in each frame
• Association: whether objects in different frames belong to the same or different objects

You are performing sports analysis using Multi-object tracking. Would you like to detect every object in the frame precisely or detect the players and their trajectories?

What is more critical for a self-driving car using a Multi-object tracking algorithm is to detect every pedestrian to avoid a collision or correctly associate the objects detected over time?