What's the most cost-effective way to label long surveillance-style videos using a frame labeling software?

The most cost-effective approach is usually not to label every frame manually. Instead, use a semi-automated workflow that combines:

1. Keyframe labeling
2. Object tracking / interpolation between frames
3. Selective sampling of frames
4. Annotation shortcuts and pre-labeling
5. Batch review for quality control

Best practical workflow

• Label only representative keyframes where the scene or object state changes.
• Use software with tracking/interpolation to propagate labels across adjacent frames.
• For long surveillance footage, sample at intervals unless every frame is truly needed.
• Use model-assisted labeling if available: let a detector pre-annotate frames, then correct them.
• Split video into event-based segments instead of annotating hours of empty footage.

Why this is cheapest

• Manual frame-by-frame labeling is the biggest cost driver.
• Surveillance videos often have lots of static frames, so labeling every frame is wasted effort.
• Tracking and interpolation reduce labor dramatically while maintaining consistency.

Good software features to look for

• Video annotation with object tracking
• Interpolation between keyframes
• Hotkeys / bulk editing
• Pre-annotation from a trained model
• Class management and QA tools
• Export to common formats like COCO, YOLO, MOT

If you want the absolute cheapest option

• Use an open-source tool like CVAT or Label Studio
• Label only keyframes and use tracking/interpolation
• Train a small model early, then use it to pre-label the rest

Rule of thumb

• If objects move slowly or scenes are stable: keyframes + interpolation
• If events are rare: event-triggered labeling
• If you need dense pixel-perfect labels for every frame: expect much higher cost, because that requirement inherently limits automation

If you want, I can also recommend the best tools for this specific use case based on whether you’re labeling bounding boxes, polygons, or segmentation masks.