There have been some research in different image background modeling methods to detect abandoned objects in highway scenes. However, traditional fixed background modeling methods easily generate foreground noises because of the environmental changes, and dynamic background modeling methods quickly integrate the motionless foreground abandoned objects into the background model. A dynamic background modeling method was proposed based on background separation Gaussian mixture model (BS-GMM) for detecting abandoned objects in highway to solve this problem. Background division method and model matching method were improved in traditional Gaussian mixture model. The weight attenuation of the Gaussian distribution models per pixel was utilized to dynamically model and update image background model. The background update frequency of the traditional Gaussian mixture model method was retained, and the stationary target was continuously detected by the method of background separation. The method can reduce the impact of environmental noises easily generated in the open environment of highway, and effectively detect the long-time motionless abandoned objects. The method can achieve the effect of real-time detection in terms of computing performance. The experimental results show that our BS-GMM method produces less foreground noises than other methods, and detects abandoned objects which are motionless for more than 20 seconds. BS-GMM method can be effectively applied to detect abandoned objects in highway.