Automated vehicle class and color profiling system based on fuzzy logic

The study proposes an automated vehicle class and color profiling system to specifically have distinct information on any apprehended car in an intelligent traffic system. The problem arises from the fact that traffic enforcers are sometimes unreliable with apprehending cars due to the lack of information on the violator. The solution is an automated system which consists of background difference method, and fuzzy logic to classify these violators. The general process is as follows: a capture picture from a traffic CCTV camera is subjected to a car detection process, and then the fuzzy inference systems are run to find the class and color of the car, and finally display a cropped picture of it along with the said descriptions. The automated car profiling system was found to have an accuracy of 99.391% for the classification process while 98.580% for the color profiling process. These results show that the algorithm is well-suited for a reliable implementation on intelligent traffic system.