%0 Journal Article
%T Recognition of Traffic Weight Using Sobel Edge Detection Method and K-Nearest Neighbor Algorithm
%A Eric John G. Emberda
%A Lovie Mae N. Dalagan
%A Christy Faith O. Baguio
%J UIC Research Journal
%D 2012
%I 
%X This study explored the use of Sobel Edge Detection and K-Nearest Neighbor algorithm in classifying the traffic weight of a given captured image. A software application was created that accepts as input, a snapshot of a given intersection. The application could determine the traffic weight of the given snapshot, as whether it is light, moderate, or heavy by comparing it to a database of images using the K-Nearest Neighbor algorithm. The accuracy of the result was highly dependent on the training data and the quality of the snapshot. Overall, the use of Sobel Edge Detection and K-Nearest Neighbor algorithm gave significant results in recognizing the weight of a given snapshot of traffic.
%K Sobel edge detection
%K k-nearest neighbor algorithm
%K traffi c weight
%K image processing
%U http://research.uic.edu.ph/ojs/index.php/uicpj/article/view/220/55