Background subtraction in traffic surveillance videos plays a crucial role in many high-level analytics and applications. Although great progress has been made in background subtraction, there are still many challenges in real traffic surveillance circumstances, such as camouflaged vehicles and dynamic background. This paper proposes a universal and accurate sampled-based vehicle detection method. The proposed method uses the color and Haar features to construct the background model, which can increase the accuracy of vehicle detection for camouflaged vehicles and is robust to low visibility. Besides, to reduce the incorrect samples of the initialized background model from a single image, the samples of the initial background model are randomly chosen from a similar candidate set. Furthermore, a novel random strategy is proposed to update the background pixel itself, while a combination update strategy with certainty and randomness is adopted for its neighborhood. This updating mechanism speeds up the suppression of ghosts in the background model. Experimental results verify the excellent behavior of our proposed method when compared to other mainstream methods.

交通监控视频中的背景扣除在许多高级分析和应用程序中起着至关重要的作用。尽管背景扣除取得了长足的进步，但在实际的交通监视环境中仍然存在许多挑战，例如伪装的车辆和动态背景。本文提出了一种通用且准确的基于采样的车辆检测方法。所提出的方法利用颜色和Haar特征来构建背景模型，可以提高伪装车辆的检测精度，并且对低能见度具有鲁棒性。此外，为了减少来自单个图像的初始化背景模型的不正确样本，从相似候选集合中随机选择初始背景模型的样本。此外，提出了一种新颖的随机策略来更新背景像素本身，而在其邻域中采用了确定性和随机性的组合更新策略。这种更新机制加快了背景模型中鬼影的抑制。实验结果证明，与其他主流方法相比，我们提出的方法具有出色的性能。