Scene segmentation based on IPCA for visual surveillance
Neurocomputing
Moving vehicles detection based on adaptive motion histogram
Digital Signal Processing
Advanced motion detection for intelligent video surveillance systems
Proceedings of the 2010 ACM Symposium on Applied Computing
Video-object segmentation and 3D-trajectory estimation for monocular video sequences
Image and Vision Computing
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
Future Generation Computer Systems
Moving object segmentation in the h.264 compressed domain
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Motion detection with pyramid structure of background model for intelligent surveillance systems
Engineering Applications of Artificial Intelligence
Automatic scene calibration for detecting and tracking people using a single camera
Engineering Applications of Artificial Intelligence
Surveillance video synopsis in the compressed domain for fast video browsing
Journal of Visual Communication and Image Representation
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Modeling background and segmenting moving objects are significant techniques for video surveillance and other video processing applications. Most existing methods of modeling background and segmenting moving objects mainly operate in the spatial domain at pixel level. In this paper, we present three new algorithms (running average, median, mixture of Gaussians) modeling background directly from compressed video, and a two-stage segmentation approach based on the proposed background models. The proposed methods utilize discrete cosine transform (DCT) coefficients (including ac coefficients) at block level to represent background, and adapt the background by updating DCT coefficients. The proposed segmentation approach can extract foreground objects with pixel accuracy through a two-stage process. First a new background subtraction technique in the DCT domain is exploited to identify the block regions fully or partially occupied by foreground objects, and then pixels from these foreground blocks are further classified in the spatial domain. The experimental results show the proposed background modeling algorithms can achieve comparable accuracy to their counterparts in the spatial domain, and the associated segmentation scheme can visually generate good segmentation results with efficient computation. For instance, the computational cost of the proposed median and MoG algorithms are only 40.4% and 20.6% of their counterparts in the spatial domain for background construction.