Landell: Most of our existing solutions, we're doing desk utilization and desk finding, but those solutions often don't have desk sensors so can you talk us through how we do desk utilization and finding?
Steve: So ACA tracking integrates with existing I.T. infrastructure. This is advantageous over sensors because they require additional maintenance and management. When you look at critical infrastructure, any issues with that hardware is going to be resolved reasonably quickly, whereas sensors are definitely a second class citizen in terms of the I.T. maintenance.
Landell: So I guess what you're saying is it's just, if you're using existing critical infrastructure it's going to be maintained as a priority. Therefore we can actually leverage that network instead of providing a whole new layer of things that are just going to fail potentially and not get seen too quickly.
Steve: Yeah exactly. And that doesn't even take into account the fact that when you leave your desk to go to the toilet your desk will be marked as free even though you've probably left your laptop and your bags and whatnot there.
Landell: Yeah that's a good point. So the desk sensors don't really relate to how people are working at a desk - we sort of come and go.
Steve: Yeah exactly. And when you look at modern workplaces, leveraging technologies such as USB C which allow people to quickly set down their laptop, plug in, and have access to multiple displays.
Landell: OK so that sort of talks to these modern workplaces are giving their employees a bit of an incentive to dock at a desk essentially. So, parking at a desk, plugging in, that gives them access to the monitors, power even and we can use that behaviour to actually track desk utilization.
Steve: Yes yes exactly. And it doesn't preclude using sensors at all. So many workplaces have breakout areas and collaboration spaces which are a bit more flexible working. And we often use cameras which. Where the image stays on the device but it can count heads in areas and this can also integrate with the lighting system and things like that. So you can replace traditional lighting sensors with a single camera sensor that performs multiple roles.
Landell: So this came up at a conference recently where they were talking about like cameras, sensors and I think that was kind of to put people at ease in the room about this isn't a camera. It's not just facial recognition. It's it's used as part of a sensor it's just picking up a face not identifying that face just picking it up as a count. And they were talking about that using it in the context of most workplaces want to try and understand how many people are in a meeting room versus what the capacity of that meeting room is. So we're not having these 12 person boardrooms only used by two or three people for each of the meetings like as a workplace we want to know how to utilize our space better, so we need that count. You mentioned that those sensors are only putting the images on the actual camera, not I'm assuming uploading them to a cloud?
Steve: Yeah exactly. So it's edge processing - it effectively just detects faces and then outputs a count which we can query.
Landell: Great. OK. So the advantage of that is that there's no I guess privacy is a concern of most people obviously, but with this the images are staying on the camera so we're not having to query anything across the Internet. It's just straight to the device that we're querying.
Steve: Yes exactly- in meeting rooms we can often leverage other hardware in there as well such as the video conference system.
Landell: So this was a really interesting project that we did across in Perth where cameras in the meeting rooms used for video conferencing could be leveraged for people counting. So talk us through that sort of solution.
Steve: Effectively what we're trying to do is reduce double handling- keep everything as simple as possible and integrate with existing solutions. So video conference systems now have features where they zoom in on the faces that are talking, but to do that they're already analyzing the images and we can use that data that they're collecting automatically and feed that back into analytics systems.
Landell: And workplaces aren't going to be used to this. They're probably used to being told you want that? Well you have to buy this, this, this, this, this, this and this at a huge expense. So how does our solution save them costs?
Steve: Well you're not buying any extra hardware. In fact we're encouraging people to use less hardware- so use more intelligent sensors that play multiple roles and that way you're managing less, your electricity costs are lower, your maintenance costs are lower, and that hardware is typically more critical infrastructure which means it's going to get priority maintenance.