GeekAlexis/FastMOT

Health v2 · maintenance only

Adoption not part of the score

1220 stars · 253 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

FastMOT is a high-performance multiple object tracking system combining YOLO/SSD detection, Deep SORT with OSNet ReID, and KLT optical flow tracking, optimized with TensorRT and Numba. It achieves real-time tracking speeds even on NVIDIA Jetson edge devices and includes camera motion compensation for moving-camera scenes.

Use cases

• track multiple people in real-time video
• count people crossing a camera view
• run object tracking on Jetson edge devices
• track objects with a moving camera
• re-identify objects that leave and re-enter the frame
• benchmark multi-object tracking on MOT challenge datasets

When to choose

• you need real-time multi-object tracking on GPU or Jetson hardware
• your camera moves and standard Deep SORT or FairMOT tracking degrades
• you want high tracking accuracy (near state-of-the-art MOTA) with fast inference
• you want to skip detector frames and fill gaps with optical flow for speed

When to avoid

• you need tracking on CPU-only machines without CUDA GPUs
• you need a maintained project with frequent updates (last release mid-2024, mostly 2021-era news)
• you need non-Python integration or a plug-and-play GUI application
• you track non-person object classes without your own detector weights

Facets

library · maturity maintenance

computer-vision image-processing video-processing machine-learning deep-learning computer-vision deep-learning artificial-intelligence python multiple-object-tracking yolo deep-sort tensorrt klt-tracker reid nvidia-jetson real-time people-counting edge-computing video linux gpu docker

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markdown · JSON · MCP: product_card(name="GeekAlexis/FastMOT")