Vehicle detection in urban traffic scenes using the Pixel-Based Adaptive Segmenter with Confidence Measurement

Abstract

The Pixel-Based Adaptive Segmenter with Confidence Measurement (PBASCM) is proposed for vehicle detection in complex urban traffic scenes to efficiently address deficiencies of the background subtraction model, which is easily contaminated by slow-moving or temporarily stopped vehicles. The background is modeled based on the history of recently observed pixel values and each pixel in the background model is assigned a confidence measurement based on the current traffic state. The foreground decision depends on an adaptive threshold, whereas the background model is updated based on the current traffic state and whether the corresponding pixel point is in the confidence period. Using real-world urban traffic videos, the overall results of detection accuracy analyses demonstrated that PBASCM achieved better performance in both qualitative and quantitative evaluations, compared with other state-of-the-art methods. PBASCM can accurately detect slowmoving or temporarily stopped vehicles, and the similarity and F-measure results for PBASCM were 0.839 and 0.912 higher, respectively, than those obtained by other methods in a traffic light sequence during the daytime. Thus, our experimental results demonstrate that PBASCM is effective and suitable for real-time vehicle detection in complex urban traffic scenes.