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Karmyug

Detection data for crowded, low-light scenes, with faces and plates removed before annotation.

Person tracking, activity recognition and anomaly data, anonymised first.

Surveillance data carries the heaviest privacy load of anything we handle, and the anonymisation has to happen before a human annotator ever sees a frame.

We redact faces and plates in the pipeline, verify the redaction, and only then route the footage to annotation. The consent and retention record travels with every clip.

Where AI is being applied in safety & surveillance

The applications teams actually come to us for, rather than the full theoretical list.

  • Person tracking

    Identity-consistent boxes across frames and camera handoffs.

  • Activity recognition

    Defined behaviour classes with agreed onset and offset.

  • Crowd density

    Counting and flow estimation in heavy occlusion.

  • Unattended objects

    Left-item detection across cluttered public scenes.

  • Low light and thermal

    Infrared and thermal footage annotated to the same schema.

  • PPE and safety compliance

    Industrial safety equipment detection on site footage.

Data security and compliance

The controls your risk team will ask about, before they ask.

  • GDPR

    EU data subject rights and DPAs

  • CCPA

    California consumer privacy

  • DPDP Act 2023

    India data protection, in-region storage

Safety & surveillance questions

How is privacy handled?

Faces and plates are redacted before annotation and the redaction is verified. Raw footage is retained only for the period the contract specifies.

Can you work with our edge model constraints?

Yes. Label density and class granularity are set with the target model size in mind during scoping.

Power your safety & surveillance AI with data you can audit.

Start with a pilot batch against your real guidelines. You accept it before anything scales.