AI Object Detection — Documentation

AI Agent

Overview

AI Object Detection brings automatic object identification to VR eye tracking studies by combining real-time YOLOv8 object detection with SightLab gaze data collection. During a session, the script captures the Vizard render window, runs YOLO on each frame to identify objects in the scene, and creates invisible 3D collision volumes at the approximate positions of detected objects. These volumes are registered as SightLab sceneObjects with gaze=True, so dwell time, view count, and other gaze metrics are collected automatically per object — no manual scene object setup required.

This allows researchers to run VR eye tracking studies where participants look around a virtual environment and the system automatically records what they looked at, for how long, and how many times — all without having to pre-label every object in the scene.

Desktop testing: The script also works in desktop mode without a headset, which is useful for testing and validating detection settings before running a full VR session.

Architecture


Vizard renders 3D scene (e.g. homeOffice)
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        v
Render window (or desktop mirror in HMD mode)
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        v
WindowCapture (finds window by title from config)
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        v
grab_yolo_frame()  -- Win32 PrintWindow -> numpy RGB array
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        v
YOLODetector (background thread, ultralytics YOLOv8)
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        v
DetectedObjectManager
   - Creates vizshape.addBox() collision volumes
   - Registers each as sightlab.addSceneObject(key, node, gaze=True)
   - Matches detections across frames to maintain persistent keys
   - Removes stale objects after OBJECT_PERSISTENCE_TIME
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        v
SightLab collects gaze/dwell data on each tracked object

Requirements

Software

Requirement	Notes
Vizard 8	WorldViz Vizard with Python 3.x
SightLab	`sightlab_utils` must be on the Python path
ultralytics	YOLOv8 — `pip install ultralytics`
opencv-python	Image processing — `pip install opencv-python`
numpy	Array handling — `pip install numpy`
pywin32	Window capture — `pip install pywin32`

Installing Dependencies in Vizard

Use Vizard's built-in Package Manager (Tools → Package Manager) or run pip directly from Vizard's Python:


"C:\Program Files\WorldViz\Vizard8\bin\python.exe" -m pip install ultralytics opencv-python numpy pywin32

Note: The first time ultralytics runs, it will download the YOLOv8 model file (~6 MB for yolov8n.pt). This requires an internet connection.

Files

File	Purpose
`AI_ObjectDetection_Config.py`	All tunable settings (model, thresholds, visuals, capture, environment)
`AI_ObjectDetection.py`	Main script — run this in Vizard

How to Run

Open AI_ObjectDetection_Config.py and verify settings (model, environment, confidence, etc.)
CAPTURE_WINDOW_TITLE must match the Vizard render window title (default: "AI_ObjectDetection"). Set to None to be prompted with a window picker at startup
Open AI_ObjectDetection.py in Vizard and press F5 (or use the "Run WinViz on Current File" task)
The script loads the configured environment (default: homeOffice.osgb)
Press Spacebar to start the trial — YOLO detection begins automatically
The participant looks around the VR scene; detected objects appear as semi-transparent boxes with labels
Press Spacebar again to end the trial
SightLab saves gaze data (dwell time, view count, etc.) per detected object to the data/ folder

Runtime Keyboard Controls

Key	Action
`Space`	Start / stop trial
`d`	Toggle debug bounding boxes on/off
`i`	Toggle YOLO overlays in HMD (keeps them on desktop mirror for researcher)
`o`	Toggle origin direction arrow
`r`	Reset viewpoint position
`p`	Toggle SightLab gaze point visibility

Configuration Reference (`AI_ObjectDetection_Config.py`)

YOLO Detection

Setting	Default	Description
`YOLO_MODEL`	`'yolov8n.pt'`	Model size. Options: `yolov8n.pt` (nano, fastest), `yolov8s.pt` (small), `yolov8m.pt` (medium, most accurate)
`YOLO_CONFIDENCE`	`0.4`	Minimum confidence threshold (0.0–1.0). Lower = more detections but more false positives
`DETECTION_INTERVAL`	`0.5`	Seconds between YOLO inference runs. Lower = more responsive, higher = less CPU
`YOLO_CLASSES`	`None`	COCO class IDs to detect. `None` = all classes. Example: `[56, 62, 63]` for chair, tv, laptop
`MAX_TRACKED_OBJECTS`	`15`	Maximum simultaneous tracked objects

3D Mapping

Setting	Default	Description
`DEFAULT_OBJECT_DEPTH`	`2.0`	Distance (meters) in front of the view where collision volumes are placed
`COLLISION_BOX_SIZE`	`[0.2, 0.2, 0.15]`	Width, height, depth (meters) of each collision volume. Thicker depth = easier gaze intersection
`OBJECT_PERSISTENCE_TIME`	`5.0`	Seconds an object survives after YOLO stops detecting it. Must be > SightLab's dwell threshold (500ms) or dwell data won't accumulate
`MATCHING_DISTANCE_THRESHOLD`	`0.5`	Max normalised screen-space distance to match a new detection to an existing tracked object of the same class. Higher = more forgiving when the user moves

Visualization

Setting	Default	Description
`SHOW_DEBUG_BOXES`	`True`	Show green semi-transparent bounding boxes over detected objects
`DEBUG_BOX_ALPHA`	`0.25`	Opacity of debug boxes (0.0–1.0)
`SHOW_LABELS`	`True`	Show 3D text labels (class name + confidence) above each object
`SHOW_OVERLAYS_IN_HMD`	`True`	Whether overlays render in the HMD at startup. Toggle with `i` key at runtime. When off, overlays still appear on the desktop mirror

Gaze Tracking

Setting	Default	Description
`ENABLE_GAZE_TRACKING`	`True`	Register detected objects as SightLab gaze targets
`USE_GAZE_BASED_ID`	`True`	Print console messages and show labels when gaze dwells on an object

Window Capture

Setting	Default	Description
`CAPTURE_WINDOW_TITLE`	`"AI_ObjectDetection"`	Window title to capture. Must match the Vizard window title. Set to `None` to be prompted with a window picker at startup
`CAPTURE_FLIP`	`None`	Flip captured frame: `0` = vertical, `1` = horizontal, `-1` = both, `None` = no flip

Other

Setting	Default	Description
`SCREEN_RECORD`	`True`	Enable SightLab's built-in screen recording
`ENVIRONMENT_MODEL`	`'sightlab_resources/environments/homeOffice.osgb'`	3D environment to load
`INSTRUCTION_MESSAGE`	(see config)	Text shown at trial start

Common COCO Class IDs

For use with YOLO_CLASSES:

ID	Class	ID	Class	ID	Class
0	person	56	chair	66	keyboard
39	bottle	57	couch	67	cell phone
41	cup	58	potted plant	73	book
46	banana	59	bed	74	clock
47	apple	60	dining table	75	vase
49	orange	62	tv/monitor	76	scissors
51	carrot	63	laptop	77	teddy bear
55	cake	64	mouse

Full list: COCO dataset classes

How Dwell Time Collection Works

SightLab tracks gaze on registered scene objects automatically. For dwell data to accumulate on a YOLO-detected object:

The object must persist with the same key across multiple frames (e.g. yolo_chair_3 stays yolo_chair_3)
The object must survive long enough for the user's gaze to exceed SightLab's dwell threshold (default 500ms)
The collision box must be thick enough for the gaze ray to intersect it

If objects are being removed and recreated too quickly (new keys each time), dwell time resets to zero. This is controlled by:

OBJECT_PERSISTENCE_TIME — how long an object survives after YOLO stops detecting it (default: 5s)
MATCHING_DISTANCE_THRESHOLD — how aggressively detections are matched to existing tracked objects (default: 0.5)
COLLISION_BOX_SIZE depth — thicker boxes are easier to hit with gaze rays (default: 0.15m)

Output Data

SightLab saves standard experiment data to the data/ folder, including per-object:

Dwell time — total time gaze rested on each detected object
View count — number of times gaze entered each object
Average dwell time — mean gaze duration per view
First view time — when the user first looked at each object
Gaze timeline — temporal sequence of gaze events

Each YOLO-detected object appears in the data with its key (e.g. yolo_chair_3, yolo_laptop_7).

Troubleshooting

Issue	Solution
No detections appearing	Check that `CAPTURE_WINDOW_TITLE` in the config matches the Vizard window title. Try setting it to `None` to use the window picker
Detections flicker / constantly reset	Increase `OBJECT_PERSISTENCE_TIME` and `MATCHING_DISTANCE_THRESHOLD`
Dwell time only recorded on one object	Same as above — objects are being recycled before dwell accumulates
`DeleteDC failed` error	The script already calls `screen_capture.stop_capture()` to prevent this. If it still occurs, ensure only one capture source is active
`ultralytics not installed` warning	Install with: `pip install ultralytics` using Vizard's Python
Vizard autocomplete spamming errors	The script uses `__import__()` for third-party packages to avoid this. If it persists, ensure no standard `import ultralytics` lines exist
Boxes appear but no gaze data	Verify `ENABLE_GAZE_TRACKING = True` and that the collision box depth is sufficient (≥ 0.1m)
Low frame rate	Increase `DETECTION_INTERVAL`, use `yolov8n.pt` (nano model), or reduce `MAX_TRACKED_OBJECTS`