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Automated traffic surveillance systems are widely used in intelligent transportation systems (ITS). However, the accuracy of video-based Vehicle Detection is heavily affected by complex environmental factors such as shadows, rain, illumination and glare. This paper introduces an approach to motion vehicle detection in complex condition over highway surveillance video. The framework is composed of two parts: background estimation and multi-feature extraction. A fast constrained Delaunay triangulation (CDT) algorithm based on constrained-edge priority is presented instead of complicated segmentation algorithms. We present a background block update modeling theory based on triangulation to estimate background self-adaptively. Consequently, we can get the difference between the current frame and the background model. After extracting features in triangular candidates, multi-feature eigenvector is created for each vehicle with Principal Component Analysis (PCA). We design a classifier to classify triangular candidate as a part of a real vehicle or not by support vector machine (SVM). And then, a parallelogram is used to represent the vehicle's shape robustly. Finally, experiments using real video sequence are performed to verify the method proposed for complex environmental factors.