Study and implementation of descriptors and classifiers for automatic detection of motorcycle on public roads

In recent years have increased the use of automated mechanisms for monitoring and enforcement of fines for traffic violations, such as radar, electronic spines and photosensors. Due to various economic and social factors use of motorcycles is gaining increasingly popular acceptance. Increasing the number of such vehicle with carelessness by conductors made to grow abruptly the number of accidents. The main security equipment of motorcyclists is the helmet but many conductors do not use it or use incorrectly. This work aims to study and implement methods for automatic detection of motorcyclists on public roads in order to, in a future work, the detection of non-use of helmets. For this purpose, transit images were used captured by video cameras. From these images different attributes have been taken through the SURF, HAAR, HOG, Fourier and K-means descriptors. And for classification of images were used classifiers as Multilayer Perceptron and Support Vector Machine.