Skip to main content

Advanced Video-Based Surveillance

  1. We propose a learning-based background subtraction approach based on the theory of sparse representation and dictionary learning. Our method makes the following two important assumptions: (1) the background of...

    Authors: Cong Zhao, Xiaogang Wang and Wai-Kuen Cham
    Citation: EURASIP Journal on Image and Video Processing 2011 2011:972961
  2. We present a new method for explaining causal interactions among people in video. The input to the overall system is video in which people are low/medium resolution. We extract and maintain a set of qualitativ...

    Authors: Neil M. Robertson and Ian D. Reid
    Citation: EURASIP Journal on Image and Video Processing 2011 2011:530325
  3. As to reduce processing load for video surveillance embedded systems, three low-level motion detection algorithms to be implemented on an analog CMOS image sensor are presented. Allowing on-chip segmentation o...

    Authors: Arnaud Verdant, Patrick Villard, Antoine Dupret and Hervé Mathias
    Citation: EURASIP Journal on Image and Video Processing 2011 2011:698914
  4. Detecting static objects in video sequences has a high relevance in many surveillance applications, such as the detection of abandoned objects in public areas. In this paper, we present a system for the detect...

    Authors: Rubén Heras Evangelio and Thomas Sikora
    Citation: EURASIP Journal on Image and Video Processing 2010 2011:858502
  5. Illumination changes cause challenging problems for video surveillance algorithms, as objects of interest become masked by changes in background appearance. It is desired for such algorithms to maintain a cons...

    Authors: M. Ryan Bales, Dana Forsthoefel, Brian Valentine, D. Scott Wills and Linda M. Wills
    Citation: EURASIP Journal on Image and Video Processing 2010 2011:171363
  6. Face detection in video sequence is becoming popular in surveillance applications. The tradeoff between obtaining discriminative features to achieve accurate detection versus computational overhead of extracti...

    Authors: Wael Louis and K. N. Plataniotis
    Citation: EURASIP Journal on Image and Video Processing 2010 2011:745487
  7. Although automatic faces recognition has shown success for high-quality images under controlled conditions, for video-based recognition it is hard to attain similar levels of performance. We describe in this p...

    Authors: Shaokang Chen, Sandra Mau, Mehrtash T. Harandi, Conrad Sanderson, Abbas Bigdeli and Brian C. Lovell
    Citation: EURASIP Journal on Image and Video Processing 2010 2011:790598
  8. In computer science, contextual information can be used both to reduce computations and to increase accuracy. This paper discusses how it can be exploited for people surveillance in very cluttered environments...

    Authors: Giovanni Gualdi, Andrea Prati and Rita Cucchiara
    Citation: EURASIP Journal on Image and Video Processing 2010 2011:684819
  9. Efficient analysis of human behavior in video surveillance scenes is a very challenging problem. Most traditional approaches fail when applied in real conditions and contexts like amounts of persons, appearanc...

    Authors: Yassine Benabbas, Nacim Ihaddadene and Chaabane Djeraba
    Citation: EURASIP Journal on Image and Video Processing 2010 2011:163682
  10. This paper studies how to improve the field of view (FOV) coverage of a camera network. We focus on a special but practical scenario where the cameras are randomly scattered in a wide area and each camera may ...

    Authors: Yi-Chun Xu, Bangjun Lei and Emile A. Hendriks
    Citation: EURASIP Journal on Image and Video Processing 2010 2011:458283
  11. This paper presents the projective particle filter, a Bayesian filtering technique integrating the projective transform, which describes the distortion of vehicle trajectories on the camera plane. The characte...

    Authors: P.L.M. Bouttefroy, A Bouzerdoum, SL Phung and A Beghdadi
    Citation: EURASIP Journal on Image and Video Processing 2010 2011:839412
  12. For the purposes of foreground estimation, the true background model is unavailable in many practical circumstances and needs to be estimated from cluttered image sequences. We propose a sequential technique f...

    Authors: Vikas Reddy, Conrad Sanderson and Brian C. Lovell
    Citation: EURASIP Journal on Image and Video Processing 2010 2011:164956
  13. A complete video surveillance system for automatically tracking shape and position of objects in traffic scenarios is presented. The system, called Auto GMM-SAMT, consists of a detection and a tracking unit. T...

    Authors: Katharina Quast (EURASIP Member) and André Kaup (EURASIP Member)
    Citation: EURASIP Journal on Image and Video Processing 2010 2011:814285
  14. Technological solutions for obstacle-detection systems have been proposed to prevent accidents in safety-transport applications. In order to avoid the limits of these proposed technologies, an obstacle-detecti...

    Authors: N Fakhfakh, L Khoudour, E. El-Koursi, J. L. Bruyelle, A. Dufaux and J. Jacot
    Citation: EURASIP Journal on Image and Video Processing 2010 2011:548604
  15. We present an approach to model articulated human movements and to analyse their behavioural semantics. First, we describe a novel dynamic and behavioural model that uses movements, a sequence of consecutive p...

    Authors: Zsolt L. Husz, Andrew M. Wallace and Patrick R. Green
    Citation: EURASIP Journal on Image and Video Processing 2010 2011:365307