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Vehicle Detection and Counting Using Adaptive Background Model Based on Approximate Median Filter and Triangulation Threshold Techniques
Automatic Control and Computer Sciences ( IF 0.6 ) Pub Date : 2020-09-14 , DOI: 10.3103/s0146411620040057 M. A. El-Khoreby , S. A. R. Abu-Bakar , M. Mohd Mokji , S. N. Omar
中文翻译:
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
更新日期:2020-09-14
Automatic Control and Computer Sciences ( IF 0.6 ) Pub Date : 2020-09-14 , DOI: 10.3103/s0146411620040057 M. A. El-Khoreby , S. A. R. Abu-Bakar , M. Mohd Mokji , S. N. Omar
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:
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