The smart home brand Eufy, by Anker, quietly ran its own test in a cash-for-clips campaign that would pay customers $2 apiece for video of package theft or checking the doors on their cars — explicitly soliciting both real-world happenings and staged reenactments to train its computer vision models. The move highlights just how valuable labeled, human-shot footage has become in the arms race to develop a smarter AI for home security.
Why Anker Wants Your Footage for Training Its AI
AI is how today’s security cameras spot people, differentiate between a falling leaf and a prowler, and reduce false alarms from an office janitor moving about. Those models only improve when they ingest huge, varied examples of the moments they’re intended to detect.
Actual thefts are rare and messy; setups are quick to shoot and brand. By paying users, Anker effectively harnesses that customer base as a giant crew of data collectors to train an event-detection algorithm to run on Eufy cameras and in the cloud.
This strategy is a reprise of other industry practices. Camera manufacturers and AI providers are increasingly mixing real footage with synthetic or acted data to fill in edge cases, lighting changes, and camera angles. The trade-off: staged clips may speed up learning but run the risk of training models on dramatized behavior that differs from what happens at an actual doorstep.
How the Eufy cash-for-clips program actually worked
Eufy advertised that it would pay $2 per submitted video in two categories of content — package theft and people trying car doors.
The company informed clients that they could re-create scenarios and even film the same act from more than one camera to increase the payouts. The goal was ambitious: tens of thousands of clips per scenario to create rich training sets.
Participants were funneled through basic consumer tools — a form for uploading videos and a PayPal address to send payments. On Eufy’s community page, over 100 commenters said they took part, implying the campaign reached a meaningful volume at a low acquisition cost per clip compared to traditional data-labeling pipelines.
From cash to badges, Eufy keeps expanding incentives
Following the experiment, Eufy expanded an existing “Video Donation Program” in its app. Instead of cash, fans who partake in events can earn digital medals, gift cards, or devices for their contributions. The program focuses on human-centric footage and incorporates an “Honor Wall” leaderboard; already, one top contributor has been credited with donating over two hundred thousand clips, a hint as to just how much raw video some homes churn out.
The company also encourages sharing clips from its baby monitors, though those uploads are couched as voluntary contributions and yield no financial incentive. Donated videos are used to enhance the AI but aren’t shared with third parties, Eufy says.
Privacy and security questions raised by the program
There are familiar questions in Eufy’s pitch: How long are videos stored, who has access to them within the company, and under what circumstances can users revoke consent after the training phase is over? Once video has been used to train a model, it’s not simple to extract that video back out. Privacy advocates such as the Electronic Frontier Foundation and policy groups like the Future of Privacy Forum also warn that there should be clear retention limits, audit trails, and a way to delete the original clips in “research” or “training” uses.
Trust is also the residue of past performance. Eufy was previously scrutinized after independent reporting revealed that its web portal had the ability to bring up a live feed from cameras without the end-to-end protections the company had claimed it offered — a problem Anker later admitted and said it would fix. Against that backdrop, gathering intimate, human-centered footage — even with incentives included and limits stated — invites increased scrutiny.
Regulators are watching. The Federal Trade Commission has threatened to classify misuse of sensitive video as unfair practices. Guidance on domestic CCTV issued by the UK Information Commissioner’s Office emphasizes necessity, minimization, and transparency. Under frameworks such as NIST’s AI Risk Management Framework and ISO/IEC 23894, data governance, documented consent, and monitoring for downstream harms are required, including in the context of consumer surveillance datasets.
The influence of staged crime videos on detection AI
Inviting customers to become actors in thefts increases the speed with which data can be gathered, but it also runs the risk of distorting the signal. People performing for a camera often overplay posture, motion, and timing — and they do it under more or less scripted lighting and angles. Meanwhile, in academic studies, models trained too much on staged or synthetic scenes can latch onto those cues and miss more subtle real-world behaviors, or return false positives.
Good practice marries real occurrences, controlled reenactments reflecting real-life conditions, and good labeling. Approaches like federated learning, on-device anonymization, or selective blurring can reduce the exposure of faces and bystanders while continuing to improve model accuracy.
What you need to know as a Eufy camera user
Before uploading any clips, customers must carefully read the program’s terms: how long data is retained, what “for AI training only” means, and whether deletion requests propagate to backup systems and derived models. Per Ring, never share any images without consent and be careful about its community guidelines for exactly what you can record in the first place (which does not include your neighbor’s yard or kids playing outside). Turn off the cloud upload feature if you don’t need it, and use in-app masking when available. If you’re not sure, ask the company for a data processing statement – and verify how to opt out of further use and delete past submissions.
The upshot: two dollars a video is a signal that says good, labeled home-security footage is fantastically rare and valuable. Whether Anker’s method will produce more intelligent alerts without sacrificing trust will hinge on its privacy protections, the soundness of its training data, and a commitment to deliver clear, user-friendly controls over the clips that fuel its AI.