Detection and description of moving objects by stochastic modelling and analysis of complex scenes

https://doi.org/10.1016/0923-5965(95)00053-4Get rights and content

Abstract

This paper presents a new technique for the detection and description of moving objects in natural scenes which is based on a statistical multi-feature analysis of video sequences. In most conventional schemes for the detection of moving objects, temporal differences of subsequent images from a video sequence are evaluated by so-called change detection algorithms. These methods are based on the assumption that significant temporal changes of an image signal are caused by moving objects in the scene. However, as temporal changes of an image signal can as well be caused by many other sources (camera noise, varying illumination, small camera motion), such systems are afflicted with the dilemma of either causing many false alarms or failing to detect relevant events. To cope with this problem, the additional features of texture and motion beyond temporal signal differences are extracted and evaluated in the new algorithm. The adaptation of this method to normal fluctuations of the observed scene is performed by a time-recursive space-variant estimation of the temporal probability distributions of the different features (signal difference, texture and motion). Feature data which differ significantly from the estimated distributions are interpreted to be caused by moving objects.

References (6)

There are more references available in the full text version of this article.

Cited by (12)

  • Change detection using a statistical model in an optimally selected color space

    2008, Computer Vision and Image Understanding
    Citation Excerpt :

    They compared their individual performances. In the work by Hötter et al. [11], gray-level differences, texture feature differences, and motion were utilized in cascade to reduce the false alarm rate in the detection process. These region-based approaches work well against image noise, but they cannot show robust results when the illumination conditions change.

  • Image sequence analysis for emerging interactive multimedia services - The European COST 211 framework

    1998, IEEE Transactions on Circuits and Systems for Video Technology
  • 3-D model-based segmentation of videoconference image sequences

    1998, IEEE Transactions on Circuits and Systems for Video Technology
  • Boosting segmentation results by contour relaxation

    2011, Proceedings - International Conference on Image Processing, ICIP
  • Smart cameras

    2010, Smart Cameras
View all citing articles on Scopus
View full text