TY - GEN
T1 - Fast Object Detection in HEVC Intra Compressed Domain
AU - Chen, Liuhong
AU - Sun, Heming
AU - Katto, Jiro
AU - Zeng, Xiaoyang
AU - Fan, Yibo
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 62031009, in part by the Shanghai Science and Technology Committee (STCSM) under Grant 19511104300, in part by Alibaba Innovative Research (AIR) Program, in part by the Innovation Program of Shanghai Municipal Education Commission, in part by the Fudan University-CIOMP Joint Fund (FC2019-001), in part by Kenjiro Takayanagi Foundation, in part by JST, PRESTO under Grant JPMJPR19M5.
Publisher Copyright:
© 2021 European Signal Processing Conference. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Conventional object detection methods are in the pixel domain and require full decoding with high computational complexity. In this paper, we propose a fast object detection method in the intra compressed domain of High Efficiency Video Coding (HEVC), which significantly accelerates the object detection process that uses compressed video images. Considering the characteristics of various coding features, we select 3 types of data for object detection, including partitioning depths, prediction modes, and residuals. To achieve a more discriminative representation of the residuals, we design an iterative restoration algorithm that can generate the details of the original image and reduce the noise in the residuals. Extensive evaluations on multiple HEVC test sequences and large-scale object detection dataset BDD100K confirm the effectiveness of our method. With a slight reduction in detection accuracy, our compressed domain detection system runs 1.8 times faster than the pixel domain.
AB - Conventional object detection methods are in the pixel domain and require full decoding with high computational complexity. In this paper, we propose a fast object detection method in the intra compressed domain of High Efficiency Video Coding (HEVC), which significantly accelerates the object detection process that uses compressed video images. Considering the characteristics of various coding features, we select 3 types of data for object detection, including partitioning depths, prediction modes, and residuals. To achieve a more discriminative representation of the residuals, we design an iterative restoration algorithm that can generate the details of the original image and reduce the noise in the residuals. Extensive evaluations on multiple HEVC test sequences and large-scale object detection dataset BDD100K confirm the effectiveness of our method. With a slight reduction in detection accuracy, our compressed domain detection system runs 1.8 times faster than the pixel domain.
KW - Compressed domain video analysis
KW - HEVC
KW - Intra frame
KW - Object detection
UR - http://www.scopus.com/inward/record.url?scp=85123212428&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123212428&partnerID=8YFLogxK
U2 - 10.23919/EUSIPCO54536.2021.9616315
DO - 10.23919/EUSIPCO54536.2021.9616315
M3 - Conference contribution
AN - SCOPUS:85123212428
T3 - European Signal Processing Conference
SP - 756
EP - 760
BT - 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
PB - European Signal Processing Conference, EUSIPCO
T2 - 29th European Signal Processing Conference, EUSIPCO 2021
Y2 - 23 August 2021 through 27 August 2021
ER -