Hailo-8

---

The hailo component in Viseron allows you to integrate Hailo-8 AI Accelerators for object detection.

Configuration​

hailo:
  object_detector:
    cameras:
      camera_one:
        fps: 1
        labels:
          - label: person
            confidence: 0.8
          - label: cat
            confidence: 0.8
      camera_two:
        fps: 1
        scan_on_motion_only: false
        labels:
          - label: dog
            confidence: 0.8
            trigger_event_recording: false

Installation​

For a Raspberry Pi 5 with the AI kit, follow the instructions in the Raspberry Pi 5 documentation.

If you have installed Ubuntu on your Raspberry Pi 5 instead of Raspberry Pi OS, you need to perform some additional steps. These steps are outlined well in this guide.

For other platforms, please refer to the official Hailo documentation.

Mounting the device​

To allow the Viseron container to access the Hailo device, you need to mount it when starting the container.

docker run --rm \
  -v {segments path}:/segments \
  -v {snapshots path}:/snapshots \
  -v {thumbnails path}:/thumbnails \
  -v {event clips path}:/event_clips \
  -v {timelapse path}:/timelapse \
  -v {config path}:/config \
  -v /etc/localtime:/etc/localtime:ro \
  -p 8888:8888 \
  --name viseron \
  --shm-size=1024mb \
  --device /dev/hailo0 \
  roflcoopter/viseron:latest

services:
  viseron:
    image: roflcoopter/viseron:latest
    container_name: viseron
    shm_size: "1024mb"
    volumes:
      - {segments path}:/segments
      - {snapshots path}:/snapshots
      - {thumbnails path}:/thumbnails
      - {event clips path}:/event_clips
      - {timelapse path}:/timelapse
      - {config path}:/config
      - /etc/localtime:/etc/localtime:ro
    ports:
      - 8888:8888
    devices:
      - /dev/hailo0

Object detector​

An object detector scans an image to identify multiple objects and their position.

Labels​

Labels are used to tell Viseron what objects to look for and keep recordings of. The available labels depends on what detection model you are using.

The max/min width/height is used to filter out any unreasonably large/small objects to reduce false positives.
Objects can also be filtered out with the use of an optional mask.

docker exec -it viseron cat /detectors/models/darknet/coco.names

Zones​

Zones are used to define areas in the cameras field of view where you want to look for certain objects (labels).
Say you have a camera facing the sidewalk and have labels setup to record the label person.
This would cause Viseron to start recording people who are walking past the camera on the sidewalk. Not ideal.
To remedy this you define a zone which covers only the area that you are actually interested in, excluding the sidewalk.

hailo:
  object_detector:
    cameras:
      camera_one:
        ...
        zones:
          - name: sidewalk
            coordinates:
              - x: 522
                y: 11
              - x: 729
                y: 275
              - x: 333
                y: 603
              - x: 171
                y: 97
            labels:
              - label: person
                confidence: 0.8
                trigger_event_recording: true

Mask​

Masks are used to exclude certain areas in the image from object detection. If a detected object has its lower portion inside of the mask it will be discarded.

The coordinates form a polygon around the masked area.
To easily generate coordinates you can use a tool like image-map.net.
Just upload an image from your camera, choose the Poly shape and start drawing your mask.
Then click Show me the code! and adapt it to the config format.
Coordinates coords="522,11,729,275,333,603,171,97" should be turned into this:

hailo:
  object_detector:
    cameras:
      camera_one:
        ...
        mask:
          - coordinates:
              - x: 522
                y: 11
              - x: 729
                y: 275
              - x: 333
                y: 603
              - x: 171
                y: 97

Troubleshooting​

logger:
  logs:
    viseron.components.hailo: debug