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Helmets are essential for the safety of a motorcycle rider, however, the enforcement of helmet wearing is a time-consuming labour intensive task. A system for the automatic classification and tracking of motorcycle riders with and without helmets is therefore described and tested. The system uses support vector machines 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.
|Number of pages||1|
|Journal||IET Intelligent Transport Systems|
|Publication status||Published - Sept 2012|
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- 1 Finished
Automatic computer based detection of motorcycle riders without helmets using state of the art computer vision techniques
Chiverton, J. & Uttama, S.
1/02/10 → 1/02/11