Deep Compression: A Compression Technology for Apron Surveillance Video

This paper presents a deep compression method that uses some object detection methods to separate moving and stationary objects from a real frame in an apron surveillance video. The extracted object image, the corresponding position information and the background image are stored in a linked list. When the video is decompressed, the extracted object images are restored to the background image according to the corresponding information, and the overall adjustment, such as illumination, is performed according to the stored information. Finally, a video with high similarity to the original video is generated. This video compression method greatly reduces the required storage space without destroying the original video information.

[1]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[2]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[3]  Mariana Afonso,et al.  Video Compression Based on Spatio-Temporal Resolution Adaptation , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[5]  Dong Liu,et al.  Neural network-based arithmetic coding of intra prediction modes in HEVC , 2017, 2017 IEEE Visual Communications and Image Processing (VCIP).

[6]  Alexandru Telea,et al.  An Image Inpainting Technique Based on the Fast Marching Method , 2004, J. Graphics, GPU, & Game Tools.

[7]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Dong Xu,et al.  Deep Kalman Filtering Network for Video Compression Artifact Reduction , 2018, ECCV.

[9]  Huimin Lu,et al.  Brain Intelligence: Go beyond Artificial Intelligence , 2017, Mobile Networks and Applications.

[10]  Xiangyu Zhang,et al.  Light-Head R-CNN: In Defense of Two-Stage Object Detector , 2017, ArXiv.

[11]  Zhenyu Liu,et al.  CU Partition Mode Decision for HEVC Hardwired Intra Encoder Using Convolution Neural Network , 2016, IEEE Transactions on Image Processing.

[12]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Xiaoyun Zhang,et al.  DVC: An End-To-End Deep Video Compression Framework , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Matti Pietikäinen,et al.  Deep Learning for Generic Object Detection: A Survey , 2018, International Journal of Computer Vision.

[15]  Huimin Lu,et al.  CONet: A Cognitive Ocean Network , 2019, IEEE Wireless Communications.

[16]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Zhan Ma,et al.  DeepCoder: A deep neural network based video compression , 2017, 2017 IEEE Visual Communications and Image Processing (VCIP).

[18]  Steve Branson,et al.  Learned Video Compression , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[19]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.