Document Type
Journal Article
Department/Unit
Department of Computer Science
Title
Object motion detection using information theoretic spatio-temporal saliency
Language
English
Abstract
This paper proposes to employ the visual saliency for moving object detection via direct analysis from videos. Object saliency is represented by an information saliency map (ISM), which is calculated from spatio-temporal volumes. Both spatial and temporal saliencies are calculated and a dynamic fusion method developed for combination. We use dimensionality reduction and kernel density estimation to develop an efficient information theoretic based procedure for constructing the ISM. The ISM is then used for detecting foreground objects. Three publicly available visual surveillance databases, namely CAVIAR, PETS and OTCBVS-BENCH are selected for evaluation. Experimental results show that the proposed method is robust for both fast and slow moving object detection under illumination changes. The average detection rates are 95.42 % and 95.81 % while the false detection rates are 2.06 % and 2.40 % in CAVIAR (INRIA entrance hall and shopping center) dataset and OTCBVS-BENCH database, respectively. The average processing speed is 6.6 fps with frame resolution 320 × 240 in a typical Pentium IV computer. © 2009 Elsevier Ltd. All rights reserved.
Keywords
Foreground detection, Moving object detection
Publication Date
2009
Source Publication Title
Pattern Recognition
Volume
42
Issue
11
Start Page
2897
End Page
2906
Publisher
Elsevier
DOI
10.1016/j.patcog.2009.02.002
Link to Publisher's Edition
http://dx.doi.org/10.1016/j.patcog.2009.02.002
ISSN (print)
00313203
Recommended Citation
Liu, Chang, Pong C. Yuen, and Guoping Qiu. "Object motion detection using information theoretic spatio-temporal saliency." Pattern Recognition 42.11 (2009): 2897-2906.