Paper Info
Title
Adaptive lossless compression of skeleton sequences
Abstract
The increasing importance of skeleton information in surveillance big data feature analysis demands significant storage space. The development of an effective and efficient solution for storage is still a challenging task. In this paper, we propose a new framework for the lossless compression of skeleton sequences by exploiting both spatial and temporal prediction and coding redundancies. Firstly, we propose a set of skeleton prediction modes, namely, spatial differential-based, motion vector-based, relative motion vector-based, and trajectory-based skeleton prediction mode. These modes can effectively handle both spatial and temporal redundancies present in the skeleton sequences. Secondly, we further enhance performance by introducing a novel approach to handle coding redundancy. Our proposed scheme is able to significantly reduce the size of skeleton data while maintaining exactly the same skeleton quality due to lossless compression approach. Experiments are conducted on standard surveillance and Posetrack action datasets containing challenging test skeleton sequences. Our method obviously outperforms the traditional direct coding methods by providing an average of 73% and 66% bit-savings on the two datasets.
Year
DOI
Venue
2020
10.1016/j.image.2019.115659
Signal Processing: Image Communication
Keywords
Field
DocType
41A05,41A10,65D05,65D17
Computer vision,Pattern recognition,Computer science,Relative motion,Coding (social sciences),Redundancy (engineering),Artificial intelligence,Skeleton (computer programming),Big data,Pattern recognition (psychology),Lossless compression
Journal
Volume
ISSN
Citations 
80
0923-5965
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Weiyao Lin173268.05
Tushar Shankar Shinde211.70
Wenrui Dai36425.01
Mingzhou Liu400.34
Xiaoyi He5122.29
Anil Kumar Tiwari66517.51
Hongkai Xiong7228.85