Quick Links

© 2019 - Impulse Geophysics Ltd - All Rights Reserved

Impulse Geophysics

19 Kingsway

Bedford

Bedfordshire

MK42 9BJ

Impulse Geophysics
Impulse Geophysics
Impulse Geophysics

Tel: 01234 309444

Fax: 01234 309555

E-Mail: info@impulsegeo.com

 

Video Redaction

Redaction by masking Personally Identifiable Information (PII)

 

There are two primary redaction steps - the localisation of PII object(s) and their obfuscation. In the method used by Impulse Geophysics the localisation is also  in two steps – locate people and then locate faces. Using this method allows a person to be obfuscated from all angles back, side and front with almost 100% redaction – beyond the Google Streetview standard.

Detection and Recognition are both achieved by using deep neural network based models.

 

Redaction of Number Plates

The detection of number plates is a common goal in computer vision. There are many ANPR cameras in operation; in police vehicles and statically mounted cameras used for speed monitoring, tax enforcement and parking enforcement.

From a machine learning perspective, detection of license plates is reasonably straightforward – the by-design uniformity of a license plate makes for a consistent pattern that a machine learning algorithm can train on. Specifically, in the UK, license plates are yellow or white rectangles with one or two rows of letters or numbers.

To detect number plates in real world street scene imagery is, however, a more complex problem. Number plates on a vehicle directly in front of the camera may be easy to detect, but number plates on parked cars may be partially occluded or present at a significant angle (for example, a car waiting to turn out of a side road), which will confuse the detection algorithm.

After considerable experimentation it was found that the best approach to detecting number plates was two parts: first examine each image for white or yellow regions, and then attempt to detect characters within the regions using optical character recognition.