Title

Selective Subtraction When The Scene Cannot Be Learned

Keywords

Background Subtraction; Dynamic Scenes; Scene Modeling

Abstract

Background subtraction techniques model the background of the scene using the stationarity property and classify the scene into two classes of foreground and background. In doing so, most moving objects become foreground indiscriminately, except for perhaps some waving tree leaves, water ripples, or a water fountain, which are typically "learned" as part of the background using a large training set of video data. We introduce a novel concept of background as the objects other than the foreground, which may include moving objects in the scene that cannot be learned from a training set because they occur only irregularly and sporadically, e.g. a walking person. We propose a "selective subtraction" method as an alternative to standard background subtraction, and show that a reference plane in a scene viewed by two cameras can be used as the decision boundary between foreground and background. In our definition, the foreground may actually occur behind a moving object. Furthermore, the reference plane can be selected in a very flexible manner, using for example the actual moving objects in the scene, if needed. We present diverse set of examples to show that: (i) the technique performs better than standard background subtraction techniques without the need for training, camera calibration, disparity map estimation, or special camera configurations; (ii) it is potentially more powerful than standard methods because of its flexibility of making it possible to select in real-time what to filter out as background, regardless of whether the object is moving or not, or whether it is a rare event or a frequent one. © 2011 IEEE.

Publication Date

12-1-2011

Publication Title

Proceedings - International Conference on Image Processing, ICIP

Number of Pages

3273-3276

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICIP.2011.6116369

Socpus ID

84856302886 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84856302886

This document is currently not available here.

Share

COinS