An Adaptive Vector-Based Vehicles Detection for Urban Intersection Camera Sensors Under Nighttime Illumination

Abstract

Vehicles detection via surveillance sensors is an intractable and essential task under nighttime urban intersection scenes. To efficiently resolve the problem that most of the current color or texture feature-based vehicles detection models easily suffer contamination from dynamic and sudden or gradual illumination variations on nighttime, an adaptive vector-based background model without complex artificial texture feature is proposed for vehicles detection in night intersection surveillance scenes. For each pixel of image, a series of filters are employed to take the values from the past at the same location and its neighborhood, and a vector is assigned to each filter to represent the neighborhood region information of the pixel. Then, the series of vectors comprising the background model and the vector of the current pixel is compared to the vectors of the background model based on the theorem of linear dependence to describe whether that pixel belongs to foreground or background. Eventually, to adapt the dynamic scenes, a random update scheme is employed to update the model. As our experimental results demonstrated in real-world nighttime urban intersection surveillance scenarios, the proposed model attains superior vehicles detection performance compared to other state-of-the-art algorithms based on extensive qualitative and quantitative evaluations.