NVIDIA Jetson Nano AI-powered Follow Trolley

Description

An assistive follow trolley project utilising AI running on the NVIDIA Jetson Nano. This code accompanies an article series on DesignSpark

Original FastMOT demonstration by GeekAlexis

Requirements

• CUDA >= 10
• cuDNN >= 7
• TensorRT >= 7
• OpenCV >= 3.3
• PyCuda
• Numpy >= 1.15
• Scipy >= 1.5
• TensorFlow < 2.0 (for SSD support)
• Numba == 0.48
• cython-bbox
• RPi.GPIO

Install for Jetson (TX2/Xavier NX/Xavier)

Make sure to have JetPack 4.4+ installed and run the script:

$ scripts/install_jetson.sh

Install for Ubuntu 18.04

Make sure to have nvidia-docker installed. The image requires an NVIDIA Driver version >= 450. Build and run the docker image:

$ docker build -t fastmot:latest .
$ docker run --rm --gpus all -it -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=unix$DISPLAY fastmot:latest

Download models

This includes both pretrained OSNet, SSD, and my custom YOLOv4 ONNX model

$ scripts/download_models.sh

Build YOLOv4 TensorRT plugin

Modify compute here to match your GPU compute capability for x86 PC

$ cd fastmot/plugins
$ make

Download VOC dataset for INT8 calibration

Only required if you want to use SSD

$ scripts/download_data.sh

Usage

• MIPI CSI camera:

  $ python3 app.py --input_uri csi://0 -gm
  

More options can be configured in cfg/mot.json

• Set resolution and frame_rate that corresponds to the source data or camera configuration (optional). They are required for image sequence, camera sources, and MOT Challenge evaluation. List all configurations for your USB/CSI camera:

  $ v4l2-ctl -d /dev/video0 --list-formats-ext
  

• To change detector, modify detector_type. This can be either YOLO or SSD
• To change classes, set class_ids under the correct detector. Default class is 1, which corresponds to person
• To swap model, modify model under a detector. For SSD, you can choose from SSDInceptionV2, SSDMobileNetV1, or SSDMobileNetV2
• Note that with SSD, the detector splits a frame into tiles and processes them in batches for the best accuracy. Change tiling_grid to [2, 2], [2, 1], or [1, 1] if a smaller batch size is preferred
• If more accuracy is desired and processing power is not an issue, reduce detector_frame_skip. Similarly, increase detector_frame_skip to speed up tracking at the cost of accuracy. You may also want to change max_age such that max_age × detector_frame_skip ≈ 30

License

MIT License 2021 GeekAlexis, 2022 RS Components Ltd