In order to detect moving objects such as vehicles in motorways, background subtraction techniques are commonly used. This is completely solved problem for static backgrounds. However, real-world problems contain many non-static components such as waving sea, camera oscillations, and sudden changes in daylight. Gaussian Mixture Model (GMM) is statistical based background subtraction method, in which values of each pixels features are represented with a few normal distributions, partially overcame such problems at least. To improve performance of GMM model, using spatial and temporal features in Lab2000HL color space which have linear hue band, is proposed in this study. The spatial and temporal features performed by using spatial low-pass filter and temporal kalman filter respectively. As a performance metric, the area under the Precision Recall (PR) curve is used. In addition to videos existing in the I2R dataset, a new dataset which images gained from traffic surveillance cameras placed over the entrance of the Istanbul FSM Bridge at different times of the day used for compare proposed method against other well-known GMM version. According to our tests proposed method has been more successful to the other methods in most cases.