Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
A Framework for Feature Selection for Background Subtraction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Using Statistical Shape Priors in Geodesic Active Contours for Robust Object Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Nonparametric Background Generation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Visual attention detection in video sequences using spatiotemporal cues
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A Visual Attention Based Region-of-Interest Determination Framework for Video Sequences*
IEICE - Transactions on Information and Systems
Background-subtraction using contour-based fusion of thermal and visible imagery
Computer Vision and Image Understanding
A Contour-Based Moving Object Detection and Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Monocular Video Foreground/Background Segmentation by Tracking Spatial-Color Gaussian Mixture Models
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Robust Foreground Detection In Video Using Pixel Layers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generating Novel Information Salient Maps for Foreground Object Detection in Video
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Variational motion segmentation with level sets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Knowledge-assisted semantic video object detection
IEEE Transactions on Circuits and Systems for Video Technology
Information theory-based shot cut/fade detection and video summarization
IEEE Transactions on Circuits and Systems for Video Technology
Unsupervised extraction of visual attention objects in color images
IEEE Transactions on Circuits and Systems for Video Technology
An Efficient Spatiotemporal Attention Model and Its Application to Shot Matching
IEEE Transactions on Circuits and Systems for Video Technology
Human action recognition using boosted EigenActions
Image and Vision Computing
Public Space Behavior Modeling With Video and Sensor Analytics
Bell Labs Technical Journal
Non-local spatial redundancy reduction for bottom-up saliency estimation
Journal of Visual Communication and Image Representation
Journal of Visual Communication and Image Representation
Spatiotemporal saliency detection and salient region determination for H.264 videos
Journal of Visual Communication and Image Representation
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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 6.6fps with frame resolution 320x240 in a typical Pentium IV computer.