Vision-based driving assistant - Domingo II, Gerardo E.; Pangan, Enrico I.; Velas, Maru M.; Miranda, Erickson T. (2000)
In this paper we describe a method for developing a Vision-Based Driving Assistant system such as a research on Artificial Intelligence, particularly Computer Vision. Given a stream of images taken using a video camera mounted on an automobile, the goal is to accurately locate and recognize encountered traffic road signs in real-time. For the computer system to classify the colors of pixels in an image from the stream of images, color segmentation is implemented using the Self Organizing Map (SOM). Using this, pixels are differentiated into the eight basic colors of the street environment, one of them being the red classification commonly used in Philippine traffic signs. The output of the SOM are then used by the Region-Growing Algorithm (RGA). RGA grows regions of red pixels until all potential traffic signs are bounded. These potential traffic signs are then fed into our Backpropagation Neural Network (BPNN), which outputs the classification of the sign. One training list and two test sets are used during the training of the BPNN. The BPNN is configured with two layers -30 nodes in the input layer and 20 nodes in the output layer. Training is conducted for 1306 epochs, where a comfortable recognition rate is achieved. The primary goal of the researchers is to develop the foundations of an Intelligent Transportation System (ITS) which automates driving. The outputs of this research can be incorporated with other ITS modules. Keywords - Self-Organizing Map (SOM), Region-Growing Algorithm (RGA), Back-Propagation Neural Network (BPNN), Intelligent Transport System (ITS).
Thesis (B.S. Computer Science)--University of the Philippines Diliman.
Keywords: Neural networks (Computer science); Computer vision; Backpropagation Neural Network (BPNN).; Region-Growing Algorithm (RGA).; Self Organizing Map (SOM).