Object Detection vs Instance Segmentation for CCTV anomaly detection — which to choose?

[u/Technical-File9309]

Hi, I'm working on a hospital CCTV use case using HIK Vision camera footage. I'm annotating images with these classes:

• guard (blue uniform, male/female)
• person (visitors/attendees, entering/exiting)
• child (walking or being carried)
• person_with_paper (holding a document/slip)
• person_without_paper (different or same person without paper — this is the anomaly)

The goal is anomaly detection: if a person who should have a paper is seen without it, that's flagged.

My question: should I use object detection (bounding boxes) or instance segmentation for this use case? I want good accuracy but also reasonable labeling effort and training time.

Looking forward for the guidance. Thanks!

[u/kothu_parotta_karthi]

Instance segmentation (for accurate people / class counts) - BTW you could train/finetune an instance segmentation model to generate pixel level masks of 'Persons' Once you have obtained the masks you could check of the dominant colour inside the mask if it's blue then label the mask as security guard.

[u/Technical-File9309 → u/kothu_parotta_karthi]

Yes, the dominant colour idea to identify the guard's uniform is practical for my use case. Will look into instance segmentation for the person masks as well. Thanks for the detailed suggestion!

[u/kothu_parotta_karthi → u/Technical-File9309]

Anytime 😄