Implementation Guidelines in the Field of Automated Driving
SegAnyMo is particularly suited for moving object detection in roadway scenarios, with specific implementation steps:
- Data preparation phase::
- Record video in MP4 format (1080p@30fps recommended) using the in-car camera
- 通过FFmpeg提取关键帧：ffmpeg -i input.mp4 -vf select=’not(mod(n,5))’ -vsync vfr img_%04d.jpg
- Model Adaptation Scheme::
- Download the road scene pre-training model (need to modify the resume_path in configs/example_train.yaml)
- Adjusting the vehicle_class parameter in core/utils/run_inference.py
- Process optimization::
- Prioritize processing-dinos semantic features (identify vehicle classes)
- Combining the -depth_anything_v2 module to get distance information
- Output of JSON-formatted trajectory data for sensing system integration
For high-speed moving objects, it is recommended to 1) shorten the -step to 3-5; 2) add the max_displacement parameter to the TAPNet configuration; and 3) use core/eval/eval_mask.py to evaluate the leakage rate.
This answer comes from the articleSegAnyMo: open source tool to automatically segment arbitrary moving objects from videoThe
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