Q3 Q4 of 2017 were quiet in people counting terms. I was contracting as a technical PM on a project to get a pan-tilt-zoom CCTV camera to automatically detect and track targets using lidar and moving thermal sensors. Automating the camera movement to track and zoom in on moving people and cars can give the benefits of a PTZ camera without the overhead of an operator.

Interesting and technically challenging work, that project has now been delivered so it’s back to counting and also to the optical detection domain!

A quick update of work in progress.

The scenes are in rough order of complexity in terms of people count. Coloured boxes show a person tracked across multiple tracks. The thinner purple/pink box shows a single track. Each unique person is tracked at least once, but full re-identification needs to be completed.

Sample Detection Videos - Work in Progress

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You upload video, we count what’s in it. Recent advances in Deep Neural Network and GPU performance mean it is now affordable to automatically count objects from generic video.

Previously, if you were counting, you would need to use manual solutions, or specialist overhead cameras. Now we can use video from mobile phones, CCTV, Gopro (tm) cameras etc.

Trials will launch in January 2017, for a service which counts objects in uploaded or web video. Customers will get a dashboard with a summary and analysis of the data. There will be a low cost per video-hour for this service.

Currently we are working on counting people, cars, bicycles, horses etc., but if there is something you would like counted in your videos, get in touch - jimd (at) yantantethera.com

• Accurate counting of objects from video
• Charged per video hour
• Fully outsouced service - just upload your video or send URLs
• People, Bicycles, Cars, Horses etc.
• Deep Neural Net technology running on powerful GPU systems
• Real world video, no configuration needed
• MOBCOUNT available for trial users January 2017

Sample Detection Videos - Work in Progress

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Thanks for watching

Research is focussed on object detection with Deep Learning, tracking in video, re-identification and real-time performance. We have worked in Internet video and camera technology for many years, but have been focussed on this area since early 2014, attending CVPR2014/2015, BMVA2015 and similar.

Current research includes YOLO, Single Shot Multibox detectors and Translation aware Fully Convolutional Networks, as well as video tubelets and person re-identification. Arxiv-sanity is our friend.

Very interested in discussing approaches, especially CNN person re-identification - jimd (at) yantantethera.com

We will be exhibiting at VISION 2016, 8-10 Nov in Stuttgart. We will be in a small :) booth IJ64 on the north side, under the company name Yan Tan Tethera. Drop an email if you are interested in booking a time - jimd (at) yantantethera.com

Technically, we are betting on the following :-

1. Deep Neural Nets will continue to improve at a rapid pace, Google/Facebook will share research as they have all the data
2. GPUs will continue to improve, though short term NVIDIA will remain dominant, mid term new chip architectures may make an impact
3. Camera technology, driven by mobile phones, will get better and better
4. Tightly controlled interaction between image sensors and software (computational photography) will become more accessible
5. The mobile phone will remain the dominant hardware and UI interaction platform
6. People will produce too much video. Other people will sell good and bad tools to deal with this