The use of motorcycles as a means of human transportation is increasingly becoming popular, especially in developing countries. This is largely because it is relatively cheap and easily accessible. Simultaneously, fatal injuries as a result of motorcycle accidents continue to rise. Extant studies have attributed this to the non-adherence to helmet rules. For example, some experts have argued that many motorists do not wear helmets when riding. To reduce the number of fatal injuries resulting from non-adherence to helmet rules, effective mechanisms for monitoring helmet-wearing are needed. This study proposes a system for automatically detecting motorists without helmets. Unlike the existing systems, the current system can detect helmets in both still images and videos of different file formats. Further, we integrate an automatic license plate recognition system for tracking motorists who do not adhere to wearing helmets. The results from the preliminary evaluation indicate high detection and prediction accuracy for both helmet detection and license plate recognition respectively