scholarly journals DC-Image for Real Time Compressed Video Matching

2014 ◽  
pp. 513-527 ◽  
Author(s):  
Saddam Bekhet ◽  
Amr Ahmed ◽  
Andrew Hunter
Keyword(s):  
Real Time ◽  
Compressed Video ◽  
Video Matching

scholarly journals Privacy Protection in Real Time HEVC Standard Using Chaotic System

Cryptography ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 18
Author(s):  
Mohammed Abu Taha ◽  
Wassim Hamidouche ◽  
Naty Sidaty ◽  
Marko Viitanen ◽  
Jarno Vanne ◽  
...  
Keyword(s):  
Real Time ◽  
Reference Frames ◽  
High Efficiency ◽  
Video Frame ◽  
Selective Encryption ◽  
Compressed Video ◽  
Secret Key ◽  
High Efficiency Video Coding ◽  
Pull Out ◽  
Bit Stream

Video protection and access control have gathered steam over recent years. However, the most common methods encrypt the whole video bit stream as unique data without taking into account the structure of the compressed video. These full encryption solutions are time and power consuming and, thus, are not aligned with the real-time applications. In this paper, we propose a Selective Encryption (SE) solution for Region of Interest (ROI) security based on the tile concept in High Efficiency Video Coding (HEVC) standards and selective encryption of all sensitive parts in videos. The SE solution depends on a chaos-based stream cipher that encrypts a set of HEVC syntax elements normatively, that is, the bit stream can be decoded with a standard HEVC decoder, and a secret key is only required for ROI decryption. The proposed ROI encryption solution relies on the independent tile concept in HEVC that splits the video frame into independent rectangular areas. Tiles are used to pull out the ROI from the background and only the tiles figuring the ROI are encrypted. In inter coding, the independence of tiles is guaranteed by limiting the motion vectors of non-ROI to use only the unencrypted tiles in the reference frames. Experimental results have shown that the encryption solution performs secure video encryption in a real time context, with a diminutive bit rate and complexity overheads.

scholarly journals Scalable FPGA Hardware Acceleration for H.264 Motion Estimation

2021 ◽  
Author(s):  
Theepan Moorthy
Keyword(s):  
Motion Estimation ◽  
Real Time ◽  
Video Compression ◽  
Reference Frames ◽  
Hardware Acceleration ◽  
Compressed Video ◽  
Time Performance ◽  
Wide Range ◽  
Multiple Reference Frames ◽  
Multiple Reference

The H.264 video compression standard uses enhanced Motion Estimation (ME) features to improve both the compression ratio and the quality of compressed video. The two primary enhancements are the use of Variable Block Size Motion Estimation (VBSME) and multiple reference frames. These two additions greatly increase the computational complexity of the ME algorithm, to the point where a software based real-time (30 frames per second (fps)) implementation is not possible on present microprocessors. Thus hardware acceleration of the H.264 ME algorithm is necessary in order to achieve real-time performance for the implementation of the VBSME and multiple reference frames features. This thesis presents a scalable FPGA-based ME architecture that supports real-time H.264 ME for a wide range of video resolutions ─ from 640x480 VGA to 1920x1088 High Definition (HD). The architecture contains innovations in both the data-path design and memory organization to achieve scalability and real-time performance on FPGAs. At 37% FPGA device utilization, the architecture is able to achieve 31 fps performance for encoding full 1920x1088 progressive HDTV video.

scholarly journals Real-time and accurate object detection in compressed video by long short-term feature aggregation

2021 ◽  
Vol 206 ◽  
pp. 103188
Author(s):  
Xinggang Wang ◽  
Zhaojin Huang ◽  
Bencheng Liao ◽  
Lichao Huang ◽  
Yongchao Gong ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Compressed Video ◽  
Short Term ◽  
Feature Aggregation

scholarly journals Scalable FPGA Hardware Acceleration for H.264 Motion Estimation

2021 ◽  
Author(s):  
Theepan Moorthy
Keyword(s):  
Motion Estimation ◽  
Real Time ◽  
Video Compression ◽  
Reference Frames ◽  
Hardware Acceleration ◽  
Compressed Video ◽  
Time Performance ◽  
Wide Range ◽  
Multiple Reference Frames ◽  
Multiple Reference

The H.264 video compression standard uses enhanced Motion Estimation (ME) features to improve both the compression ratio and the quality of compressed video. The two primary enhancements are the use of Variable Block Size Motion Estimation (VBSME) and multiple reference frames. These two additions greatly increase the computational complexity of the ME algorithm, to the point where a software based real-time (30 frames per second (fps)) implementation is not possible on present microprocessors. Thus hardware acceleration of the H.264 ME algorithm is necessary in order to achieve real-time performance for the implementation of the VBSME and multiple reference frames features. This thesis presents a scalable FPGA-based ME architecture that supports real-time H.264 ME for a wide range of video resolutions ─ from 640x480 VGA to 1920x1088 High Definition (HD). The architecture contains innovations in both the data-path design and memory organization to achieve scalability and real-time performance on FPGAs. At 37% FPGA device utilization, the architecture is able to achieve 31 fps performance for encoding full 1920x1088 progressive HDTV video.

scholarly journals Anomalous Human Activity Recognition in Surveillance Videos

2019 ◽  
Vol 8 (2S7) ◽  
pp. 350-355
Keyword(s):  
Real Time ◽  
Video Compression ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Abnormal Behavior ◽  
Compressed Video ◽  
Surveillance Video ◽  
Surveillance Videos ◽  
Adaptive Video

This paper is a survey on different approaches for Human Activity recognition which has utmost significance in pervasive computing due to its many applications in real-life. Human-oriented problems such as security can be easily taken care of by detecting abnormal behavior. Accurate human activity recognition in real-time is challenging because human activities are complicated and extremely diverse in nature. The traditional Closed-circuit Television (CCTV) system requires to be monitored all the time by a human being, which is inefficient and costly. Therefore, there is a need for a system which can recognize human activity effectively in real-time. It is time-consuming to determine the activity from a surveillance video, due to its size, hence there is a need to compress the video using adaptive compression approaches. Adaptive video compression is a technique that compresses only those parts of the video in which there is least focus, and the rest is not compressed. The objective of the discussion is to be able to implement an automated anomalous human activity recognition system which uses surveillance video to capture the occurrence of an unusual event and caution the user in real-time. So, the paper has two parts that include adaptive video compression approaches of the surveillance videos and providing that compressed video as the input to detect anomalous human activity

Sign in / Sign up

Export Citation Format

Share Document