Multi-object tracking (MOT) in video analysis is increasingly in demand in many industries, such as live sports, manufacturing, surveillance, and traffic monitoring. For example, in live sports, MOT can track soccer players in real time to analyze physical performance such as real-time speed and moving distance.
Previously, most methods were designed to separate MOT into two tasks: object detection and association. The object detection task detects objects first. The association task extracts re-identification (re-ID) features from image regions for each detected object, and links each detected object through re-ID features to existing tracks or creates a new track. It’s challenging to do real-time inference in an environment with a large number of objects. This is because two tasks extract features respectively and the association task needs to run re-ID feature extraction for each object. Some proposed one-shot MOT methods add a re-ID branch to the object detection network to conduct

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