A survey on compressed domain video analysis techniques

被引:47
|
作者
Babu, R. Venkatesh [1 ]
Tom, Manu [1 ]
Wadekar, Paras [1 ]
机构
[1] Indian Inst Sci, SERC, Video Analyt Lab, Bangalore 560012, Karnataka, India
关键词
Video object segmentation; Human action recognition; Indexing; Retrieval; Face detection; Video classification; Object tracking; Object localization; Moving object detection; H.264/AVC; HEVC; MPEG; Compressed domain; Quantization parameter; Motion vectors; Transform coefficients; Video analysis; MOVING OBJECT SEGMENTATION; SPATIOTEMPORAL SEGMENTATION; MOTION; RETRIEVAL; RECOGNITION; CLASSIFICATION; EFFICIENCY; TRACKING; SPEED;
D O I
10.1007/s11042-014-2345-z
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.
引用
收藏
页码:1043 / 1078
页数:36
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