1 image per alert. Right now we are not ingesting short clips because we wanted to minimize response time. As we further test the performance of handling small clips, we may incorporate into service or we may decide going on-prem is the better way forward. TBD.
Changing my first answer (below) after re-reading question. If the first alert does not detect human activity, we still analyze the second frame. We skip alerts when we recognize the "event" has already been detected and alerted upon. For example, if the kids are playing in the backyard, the backyard camera will send an alert the first time the activity is detected. However, if the kids are playing for 30 minutes, you will not receive alerts for the next 30 minutes while the kids continue to play. The software is smart enough to know it is the same "event".
You will find that local detection will work and sell better. The biggest hurdle for local detection is processing power.
Agreed. Going on-prem is a priority. Hardware is improving quickly and becoming more cost-effective to run AI.
For closed systems like mine, using only a VPN and no open ports, will this still work?
Most of them do not have the blue highlighting but some do have the highlighting in contact with the human.
We continuously train the computer vision model to get smarter. Regarding false negatives, i.e. humans were in the scene but not detected, our testing shows a 95%+ accuracy rate, i.e. we almost always detect humans when they are visible somewhere in the camera. This includes images with partial views of a human, body parts as well as images where the person is not even facing the camera, e.g. walking away as shown in other sample videos on the website.
Yes! We received this feedback from other customers as well. Keep in mind, with the Blue Iris integration, text alerts will stop and you will receive alerts through the Blue Iris alerting system - mobile app, website or desktop server.
That is great news! I would much rather receive push alerts via Blue Iris than text. Definitely looking forward to the go-live.
We feel like we have a good handle on daytime images. A lot of our R&D effort now is on making night time surveillance more and more accurate.
This is definitely good to know. Are there plans to have other detection types, such as vehicles?
To be honest, we already have a computer vision model to detect vehicles.
We use it for aerial surveillance use cases for drones. Would be interested in knowing what you had in mind with Blue Iris.
That is great news. I’d be interested to know if a car pulled into my driveway, and receive a notification of vehicle make/model/color, if that is possible.To be honest, we already have a computer vision model to detect vehicles.
