MPP: A Novel Algorithm for Estimating Vehicle Space Headways from a Single Image

Vehicle space headway, also called spacing, is an important and basic traffic parameter. Traditional space headway calculation methods are facing the problems of large errors and high costs. This paper presents a novel algorithm based on measurement point pairs (MPPs) to estimate the real-time microcosmic vehicle space headway from single images in existing traffic surveillance videos and images without any additional equipment. First, the camera is calibrated with road markings to obtain the relationship between the image coordinates and the world coordinates. Second, vehicle pairs of two successive vehicles in the image are established, measurement points on each vehicle are selected by video intelligence analysis technologies, and their world coordinates are calculated by camera calibration results. Finally, the measurement points of the preceding and following vehicles are matched to obtain the MPPs, followed by the calculation of the weighted space headway. By using the measurement point information, one of the most difficult problems in image distance measurement, the lack of height information, is solved. The main factors causing estimation errors are fully addressed and the range and trend of errors under certain conditions are given by virtual simulation. Two real-world experiments are used to prove the accuracy and usability of the MPP in common video scenes: the simulation experiment indicates that the MPP algorithm achieves a high accuracy with estimation error less than ±0.1 m and the relative error within 1.1%; the application experiment shows that the MPP-based calculation is more accurate and stable than the state-of-the-art distance measurement algorithm and that the convenience of the proposed MPP algorithm is higher than that of traditional methods of space headway estimation. Document type: Articl

Dynamic background subtraction method based on spatio‐temporal classification

The dynamic background will cause extremely negative effects on background subtraction and is difficult to eliminate. This study proposes a dynamic background subtraction method based on a spatio‐temporal classification which mainly contains two key steps: temporal and spatial classifications. For temporal classification, the closest pixel sampling algorithm is used to sample background pixels in groups, which avoids centralised sampling and a complicated mathematical modelling process. For the background model obtained by group sampling, the pixels which are similar to the detected pixel are classified into the same category. According to the number of pixels in this category, the label (foreground or background) of the detected pixel can be determined thus a coarse foreground mask is obtained. For spatial classification, considering the correlation between dynamic background pixels and neighbouring pixels, a square window can be set for each foreground pixel in the coarse mask, and then all pixels in the window classified. According to the labels of these classified pixels, a more accurate foreground mask is obtained. The experiments on public datasets demonstrate that the proposed method outperforms other state‐of‐the‐art methods