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This paper presents an approach of model-oriented road detection based on trapezoidal model proposed by H. Jeong, et al and fuzzy Support Vector Machine (SVM). Firstly, the frames ex-tracted from the video are preprocessed by Pulse Coupled Neural Network (PCNN), and then handled by Kalman filter and Expectation Maximization (EM) algorithms. Next, according to the road’s dif-ferent feathers, using fuzzy algorithm chooses a corresponding SVM for further lane detection, and then using morphological filters obtains the final detecting result. For different types of roads, this method uses fuzzy algorithm to choose different SVMs. Furthermore, in preprocessing using PCNN removes the shadow in the road to reduce the effect of illumination variations. Experimental results show that our method can receive better lane detecting results than the trapezoidal model and BP proposed by H. Jeong, et al..
This paper presents an approach of model-oriented road detection based on trapezoidal model proposed by H. Jeong, et al and fuzzy Support Vector Machine (SVM). Firstly, the frames ex-tracted from the video are preprocessed by Pulse Coupled Neural Network ( PCNN), and then handled by Kalman filter and Expectation Maximization (EM) algorithms. Next, according to the road’s dif-ferent feathers, using fuzzy algorithm chooses a corresponding SVM for further lane detection, and then using morphological filters procurement the final detecting result In addition, in preprocessing using PCNN removes the shadow in the road to reduce the effect of illumination variations. Experimental results show that our method can receive better lane detecting results than the trapezoidal model and BP proposed by H. Jeong, et al.