3D Neural Model-Based Stopped Object Detection

  • Lucia Maddalena
  • Alfredo Petrosino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


In this paper we propose a system that is able to distinguish moving and stopped objects in digital image sequences taken from stationary cameras. Our approach is based on self organization through artificial neural networks to construct a model of the scene background and a model of the scene foreground that can handle scenes containing moving backgrounds or gradual illumination variations, helping in distinguishing between moving and stopped foreground regions, leading to an initial segmentation of scene objects. Experimental results are presented for video sequences that represent typical situations critical for detecting vehicles stopped in no parking areas and compared with those obtained by other existing approaches.


moving object detection background subtraction background modeling foreground modeling stopped object self organization neural network 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lucia Maddalena
    • 1
  • Alfredo Petrosino
    • 2
  1. 1.ICAR - National Research CouncilNaplesItaly
  2. 2.DSA - University of Naples ParthenopeNaplesItaly

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