Computer Vision for Traffic Safety - Helmet detection and tracking

Computer Vision for Traffic Safety

Paper: Helmet presence classification with motorcycle detection and tracking, IET ITS, 6(3), 2012, http://dx.doi.org/10.1049/iet-its.2011.0138

Helmets are essential for the safety of a motorcycle rider however the enforcement of helmet wearing is a time consuming labour intensive task. I therefore developed a system for the automatic classification and tracking of motorcycle riders with and without helmets. The system used Support Vector Machines (SVM)s trained on histograms derived from head region image data of motorcycle riders using both static photographs and individual image frames from video data. The trained classifier is incorporated into a tracking system where motorcycle riders are automatically segmented from video data using background subtraction. The heads of the riders are isolated and then classified using the trained classifier. Each motorcycle rider results in a sequence of regions in adjacent time frames called tracks. These tracks are then classified as a whole using a mean of the individual classifier results. Tests show that the classifier is able to accurately classify whether riders are wearing helmets or not on static photographs. Tests on the tracking system also demonstrate the validity and usefulness of the classification approach.

Funded by grant  / award 

 Jan 2010–Jan 2011

Grant: Automated Computer Based Detection of Motorcycle Riders Without Helmets Using State of the Art Computer Vision Techniques

Funding: THB 150000 • Mitsui Sumitomo Insurance Group (MSIG) Welfare Foundation

Principal Investigator: Dr John Chiverton  Mae Fah Luang University

Co-investigators: Dr Surapong Uttama  Mae Fah Luang University