Abstract

Background subtraction method is widely used for vehicle detection. One of the issues in this method is to find a suitable and accurate background model that works in all conditions. Moreover, setting an appropriate threshold value to discriminate between the moving objects and stationary background plays a crucial role in increasing the detection performance. In this paper, an adaptive background model combined with an adaptive threshold method is proposed. It is demonstrated that the proposed method can efficiently differentiate between moving vehicles and background in urban roads under different weather conditions (i.e., normal, rainy, foggy, and snowy). Comparisons between the proposed method and other methods, such as the adaptive local threshold (ALT) and the three frame-differencing methods show the potential of our approach. The experimental results show that the proposed method increases the average recall value by 16.4% and the average precision value by 21.7% in comparison to the ALT method with a negligible increase in the processing time.

中文翻译：

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基于近似中值滤波和三角化阈值技术的自适应背景模型车辆检测与计数

摘要

背景扣除法被广泛用于车辆检测。这种方法的问题之一是找到一种在所有条件下都能正常工作的合适且准确的背景模型。此外，设置适当的阈值以区分运动对象和静止背景在提高检测性能方面起着至关重要的作用。本文提出了一种结合自适应阈值方法的自适应背景模型。结果表明，所提出的方法能够有效地区分不同天气条件下（正常，多雨，有雾和下雪）的行驶车辆与城市道路背景。所提出的方法与其他方法（如自适应局部阈值（ALT）和三种帧差分方法）之间的比较表明了本方法的潜力。