Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22018
Title: Real-time moving object segmentation and tracking for H.264/AVC surveillance videos
Authors: Dong, P
Xia, Y
Zhuo, L
Feng, D
Keywords: H.264 compressed domain
Prediction mode
Segmentation and tracking
Video surveillance
Issue Date: 2011
Publisher: IEEE
Source: 2011 18th IEEE International Conference on Image Processing (ICIP), 11-14 September 2011, Brussels, p. 2309-2312 How to cite?
Abstract: With increased use of H.264/AVC in various applications including video surveillance systems, feature extraction and knowledge representation in compressed domain are becoming attractive. A real-time H.264/AVC compressed domain moving object segmentation and tracking algorithm for surveillance videos is proposed in this paper. This algorithm consists of moving object detection, bounding box matching, spatiotemporal merge and split reasoning and trajectory smoothing, with major innovation in incorporating the information provided by the prediction modes into the framework of motion detection and trajectory construction. The experimental results on both indoor and outdoor surveillance videos demonstrate that the adaptive use of the information from motion vectors, DCT coefficients and prediction modes can substantially improve the performance of moving object segmentation and tracking.
URI: http://hdl.handle.net/10397/22018
ISBN: 978-1-4577-1304-0
978-1-4577-1302-6 (E-ISBN)
ISSN: 1522-4880
DOI: 10.1109/ICIP.2011.6116063
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

3
Last Week
0
Last month
Citations as of Feb 26, 2017

Page view(s)

32
Last Week
4
Last month
Checked on Aug 14, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.