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access icon free Vehicles detection for illumination changes urban traffic scenes employing adaptive local texture feature background model

  • Author(s): Yunsheng Zhang 1, 2, 3 ; Chihang Zhao 4 ; Wen Shi 2 ; Kaijun Leng 2
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  • Source: Volume 12, Issue 10, December 2018, p. 1283 – 1290
    DOI:  10.1049/iet-its.2018.5298 , Print ISSN 1751-956X, Online ISSN 1751-9578
© The Institution of Engineering and Technology
Received 19/08/2017, Accepted 09/07/2018, Revised 04/06/2018, Published 06/09/2018

An Adaptive Local Texture Feature Background Model (ALTF-BM) is proposed to resolve the deficiency in current background models, which are easily contaminated by sudden and gradual illumination changes in complex urban traffic scenes. Based on Weber's law, the authors first develop Adaptive Local Texture Feature (ALTF), calculated over a predefined local region around the pixel employing an adaptive distance threshold, and then the background is modelled on the base of sample consensus scheme using the calculated features. Furthermore, to label the foreground pixels, the difference between the background model and input video frames is then directly compared by ALTF encoding. Finally, the model is updated using the random update policy to adapt to the changing illumination and the dynamic background. The experimental results on real-world urban traffic videos and the public Change Detection benchmark of 2014 (CDnet2014) show that the proposed ALTF-BM offers the best performance compared to the other state-of-the-art texture-based methods, and the average F-measures and similarity results of the proposed ALTF-BM are 0.547 and 0.393 higher than benchmarks on the night traffic-light sequence, respectively. The encouraging experimental results demonstrate the efficiency of the proposed ALTF-BM in handling sudden and gradual illumination changes in urban traffic scenes.

Inspec keywords: image texture; road traffic; object detection; image colour analysis; traffic engineering computing; image segmentation

Other keywords: complex urban traffic scenes; gradual illumination changes; dynamic background; public Change Detection benchmark; adaptive distance threshold; ALTF-BM; sudden illumination changes; predefined local region; adaptive local texture feature background model; current background models; real-world urban traffic videos

Subjects: Computer vision and image processing techniques; Traffic engineering computing; Optical, image and video signal processing

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