Ultralytics YOLO26: compare six object trackers

This walkthrough compares six object trackers in the Ultralytics YOLO26 framework using a Python benchmarking script. The speaker explains how model.track adds object IDs to detections so applications can follow objects across video frames, count crossings, or build heat maps.

The code walkthrough covers metrics collection, track statistics, annotations, and a benchmark class that processes video and writes annotated clips. Command line arguments select the source, model, and trackers. The example supports video or webcam input and standard or custom model weights. The model downloads automatically on the first run.

The benchmark measures combined detection and tracking speed, alongside latency, ID counts, fragmentation, average track lifetime, and a stability metric. In the MacBook CPU demonstration, ByteTrack reaches roughly 50 FPS. The speaker reports that ByteTrack is fastest in this test, while TrackTrack has the strongest ID stability and longest track lifetime but runs slowest.

The speaker favors FastTrack as a compromise between speed and stability, though the example still shows ID switches. TrackTrack also appears to miss some track starts, which the speaker suggests may require configuration changes. An interactive HTML report lets viewers compare metrics and replay annotated clips. These results describe the demonstrated workload rather than a universal tracker ranking.