Zircon assists Synoptix Ltd with a feasibility study to detect objects within video footage and images.

Operating Systems: Windows
Languages: Python
Other: Blob Detection,  Casscade Classifier,  Colour Comparison,  Contour Matching,  Edge Detection,
Histogram Of Oriented Gradients,  Image Comparison,  Sliding Windows,  Subcomponent Classifier,  SURF Descriptors

 

In the past much of our work has been on projects related to the transport industry. What’s more, we usually have a pretty clear idea what the solution is going to look like. But that’s not always the case. We recently completed a project related to defence and security (we can’t tell you much more than that as…well, we just can’t!). And although the requirements were clearly defined the end goal was merely to test the feasibility of an idea and outline the most promising ways forward.
Background To The Project

Our immediate client was Synoptix Ltd, a provider of bespoke, high level Systems and Safety Engineering solutions for a wide range of different industrial sectors. They were working on a project for the Defence Science and Technology Laboratory (DSTL), the centre of scientific excellence for the UK Ministry of Defence (MoD). Its stated aim is “to maximise the impact of science and technology for the defence and security of the UK.”

Synoptix was collaborating with DSTL in a project that involved the development of object detection software. As Synoptix had no in-house software capability they were looking to partner with a suitable third party.

The Head of Sales for Synoptix was familiar with Zircon and knew we had recently been working on a project with similarities to DSTL’s requirements but within the rail industry environment. So we were asked to submit a proposal, which was accepted.

“I’d definitely endorse them with regard to their quality of communication, the quality of documentation they produced.”

Ben Durant

Operations Manager, Synoptix

The Challenge

The general idea, in layman’s terms, was to get a better understanding of how far one could go in developing an effective object detection solution using Commercial Off The Shelf (COTS) software and libraries. The task was to see how difficult it would be to get a conceptual solution through to Technology Readiness Level (TRL) 3 via the development of algorithms on an open-source image processing platform.

More specifically, the software had to be capable of detecting the presence of specific pre-determined objects within a digital image (either a still photograph or the individual frame of a video). The program had to be capable of picking out the shape of an object from whatever else was going on around it, in a variety of different conditions and with a range of different image formats. What’s more, with video footage it had to be capable of tracking the shape through multiple frames. Being even more specific the system was required to:

  • Recognise objects by referring to still images stored in the system
  • Categorise objects detected
  • Sub-categories objects where possible
  • Perform recognition on video stream shot at any viewing angle; any compression; any frame rate; any illumination; with any level of clutter; with any confusers or obscuration; and at any viewing range, from 5 to 100 metres
  • Operate in visible spectrum (RGB and grey scale)
  • Track detected objects through scene
  • Create a timestamped list of when objects appear in video

So it was a very tall order!

However, the end deliverable was not a fully functioning, tested and effective system. It was to see how far one could go down that road using COTS software and libraries; then present those findings, alongside Synoptix, to the DSTL.

How Things Progressed

The primary COTS component we proposed was OpenCV, which is a commonly used open source computer vision library. This provided much of the actual algorithmic functionality utilised by the object detection software we developed. Because the project was essentially an R&D exercise we selected Python 2.7.5 as the development language as we find code can be written very quickly in Python. This also meant that the software could easily be ported to C++, and better performance achieved, should a release version be required.

Ben Durant, Operations Manager at Synoptix, explains that it proved a very hard task. “It was very difficult, much more so than the project they had undertaken on the railways. The system had to work under so many different conditions and situations that they were confronted with a variety of awkward challenges.”

“They tackled these in a variety of different ways. They tried doing co-ordinate point detection, but that didn’t work very well. They tried turning those shapes into shadows, that didn’t work very well either. Then they ended up using a couple of filters for image processing, and these were reasonably good at detecting the shapes we were asked to look for. They got it to work at the testing stage, but it was a long way off a finished product.”

“I found Zircon very easy to work with, They were very cooperative, very helpful throughout that process”

Ben Durant

Operations Manager, Synoptix

Conclusion

Ben makes the point that “The key deliverable was not a product that was ready to be deployed. It was the production of a joint report, prepared in partnership between us and them, to outline the lessons learned during the exercise.” He adds that “They were very good at reporting what was going on and keeping us in the loop. This was essential, because the exercise was essentially an information gathering one, so good communication was very important.”

Explaining this point in more detail he comments that “I worked closely with the main developer at Zircon and we had to thoroughly analyse what was going on and where the technical difficulties lay. We had to make a lot of decisions on what findings were most meaningful and how best to frame them for presentation to the MOD.”

He concludes that “I found Zircon very easy to work with. The process involved a lot of discussion and negotiation. They were very cooperative, very helpful throughout that process. I’d definitely endorse them with regard to their quality of communication, the quality of documentation they
produced. In the end we successfully delivered a presentation to the MOD, albeit with limited functionality, that met their specification. It proved to be a very valuable piece of research.”

His final comment was “If we get some further work from the MOD on this I’ll be teaming up with Zircon, no question.”

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