Measuring traffic flow and classifying vehicle types: A surveillance video based approach

Abstract

The paper presents a vehicle counting method based on invariant moments and shadow aware foreground masks. Estimation of the background and the segmentation of foreground regions can be done using either the Mixture of Gaussians model (MoG) or an improved version of the Group Based Histogram (GBH) technique. The work demonstrates that, even though the improved GBH method delivers performance just as good as MoG, considering computational efficiency, MoG is more suitable. Shadow aware binary masks for each frame are formed by using background subtraction and shadow removal in the Hue Saturation and Value (HSV) domain. To determine new vehicles in the subsequent frame (in addition to those in the current frame), invariant moments are used. For vehicles which are the same model and brand, color information and distance between center of mass and an imaginary reference line need to be considered. As for classification, the paper proposes a new method based on perspective projection of the scene geometry. The classification is grouped into three major tracks: bikes, saloon cars, and long vehicles. For each category, a lower and an upper bounding curve are developed to show the extent of their associated modality regions.