NDLI: A fast and accurate video object detection and segmentation method in the compressed domain

Content Provider	IEEE Xplore Digital Library
Author	Zhanhui Wang Guizhong Liu Long Liu
Copyright Year	2003
Description	Author affiliation: Sch. of Electron. & Inf. Eng., Xi'an Jiaotong Univ., China (Zhanhui Wang; Guizhong Liu; Long Liu)
Abstract	In this paper, we present a fast and accurate method for object detection and segmentation in the compressed domain. First the motion vectors are emendated by our spatial confidence and correction rule so that these can represent real motions of objects. Then the initial location and a coarse segmentation from the motion vectors are obtained by applying the EM algorithm. For there are DCT coefficients only in I frames, the coarse masks are mapped into I frames through the motion parameters. These blocks in the masks can be decompressed to obtain details of a specific object in the pixel domain. The actual edges of the objects can be extracted by applying Canny Edge Detection only in the segmented regions. By using the proposed algorithm, the amount of data needed to be processed is kept the necessarily minimal, saving the computational time as well as gaining the pixel-wise edges of the segmented objects.
Sponsorship	IEEE Circuits and Syst. Soc. (CASS) IEEE R10 CASS IEEE Beijing Section Chinese Neural Networks Council Circuits and Information Processing Committee, CASS-CIE Southeast Univ., China
Starting Page	1209
Ending Page	1212
File Size	266013
Page Count	4
File Format	PDF
ISBN	0780377028
DOI	10.1109/ICNNSP.2003.1281087
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Image coding Image edge detection Statistical distributions Clustering algorithms Object detection Video compression Data mining Face detection Discrete cosine transforms
Content Type	Text
Resource Type	Article

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