AI Case Study

"FlowX and Vivacity are helping transport authorities react quicker to traffic incidents. Our aim is to reduce the detection time for traffic operators from 10 minutes to 10 seconds. To do this, we are integrating with authorities’ existing CCTV camera network.

Step #1 — We integrate with a transport authority’s existing CCTV network. We ensure to have in place extremely robust data security agreements.

Step #2 — We use the latest computer vision techniques to anonymously classify objects (car / pedestrian / cyclist / truck etc).

Step #3 — We track these anonymous, classified objects across the screen — creating anonymous, granular data (counts / occupancy / speed / path etc).

Step #4 — We use this anonymous, granular data as an input into machine learning models to flag when traffic conditions are abnormal.

Outcomes from Phase 1
#1 — First of a Kind
To the best of our knowledge, this is the UK’s first integration of deep neural networks on existing highways CCTV cameras in production.

We have successfully integrated with a total of 14 cameras across Devon, Leeds, and Exeter Councils.

#2 — Repeatable data privacy agreements
Data privacy is rightly a primary concern for stakeholders at local authorities, so we have in place extremely robust data security agreements.

#3 — Integrated with analogue cameras
Exeter & Devon Council cameras are analogue. We therefore needed to install Analogue-to-Digital converters for each feed. This provides us with confidence to tackle the large proportion of existing public CCTV feeds which remain analogue.

#4 — Solved the Pan-Tilt-Zoom (PTZ) problem
Most existing public CCTV cameras in the UK are not fixed, and instead can be moved by an operator for a better view of the roadspace. This means a simplistic system returns erroneous data after a PTZ camera is moved.
We therefore wrote software which can automatically ‘understand where it is looking’. This method successfully identifies a pre-set view at any time of day, even if the position is 30% out of alignment from the exact pre-set in direction or zoom. The count line and zone positions adjust automatically.

#5 — Data for strategic decision making
We are continuously extracting anonymous, classified data from the CCTV feeds. This data itself is useful for longer-term strategic decision making, providing:
Reliable, city-wide, real-time information
Of vehicle traffic, cyclists and pedestrians

#6 — Prototype system of real-time incident detection
Using unsupervised anomaly detection, we flag when traffic conditions deviate past a configurable standard deviation — based on the norm for that day of week at that time of day. Traffic conditions are defined by their flow (counts / occupancy / speed / path etc).
Real-time incident detection helps operational traffic control centres:

* Respond to incidents in real-time
* Preventing the build-up of congestion
* While being less resource-intensive

Transition into Phase 2
Our key users are traffic operators in traffic control centres. Our principal priority moving forward is to add genuine value to our users, building a robust system of real-time incident detection which seamlessly complements existing workflows."