道路交通场景监控视频编码研究

doi:10.3778/j.issn.1002-8331.2002-0401

计算机工程与应用 ›› 2021, Vol. 57 ›› Issue (10): 187-193.DOI: 10.3778/j.issn.1002-8331.2002-0401

道路交通场景监控视频编码研究

蔡鉴明，张贤贤，梁月

1.中南大学交通运输工程学院，长沙 410075
2.智慧交通湖南省重点实验室，长沙 410075

出版日期:2021-05-15 发布日期:2021-05-10

Research on Surveillance Video Coding in Road Traffic Scene

CAI Jianming, ZHANG Xianxian, LIANG Yue

1.College of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
2.Smart Transport Key Laboratory of Hunan Province, Changsha 410075, China

Online:2021-05-15 Published:2021-05-10

摘要/Abstract

摘要：

道路交通监控摄像机所能拍摄的最大场景范围已知，分析监控视频中交通流流动的规律，提出基于场景的压缩方法。通过运动在空间上的分布，建立运动能量图，取反获取掩模。改进HEVC编码方法，针对运动分布区域交通流的方向性强且运动剧烈特征，使用非对称模板改进TZSearch运动搜索算法；针对掩模区域运动性弱特征，提前设置运动搜索终止阈值。实验结果表明，与AVS2和MPEG-4编码相比，改进方法能够同时保证交通显著性以及压缩性能；与HEVC编码比较，改进方法的平均比特率节约、平均PSNR增效和平均时间节约分别为11.80%、3.90 dB和5.55%，车辆识别的准确率能够提高7.41%，增强了视频编码效率的同时强化了视频本身的分析性能。

关键词: 智慧交通, 交通监控, 视频压缩, HEVC标准, 运动能量图

Abstract:

The maximum range of scenes that can be captured by road traffic surveillance cameras is known. By analyzing the traffic flow in the surveillance video, a scene based compression method is proposed. Based on the distribution of motion in space, the motion energy map is established and the mask is obtained by negation. The HEVC coding method is improved：According to the characteristics of strong directivity and violent motion of the traffic flow in the motion distribution area, an asymmetric template is used to improve the TZSearch motion search algorithm; and for the characteristics of weak motion in the mask area, a motion search termination threshold is set in advance. The experimental results show that compared with AVS2 and MPEG-4 encoding, the improved method can ensure both traffic saliency and compression performance; and compared with HEVC encoding, the average bit rate saving, the average PSNR enhancement and the average time saving of the improved method are11.80%, 3.90 dB and 5.55%, respectively. The accuracy of vehicle identification can be improved by 7.41%, which enhances the video encoding efficiency and the analysis performance of the video itself.

Key words: intelligent transportation, traffic surveillance, video compression, High Efficiency Video Coding（HEVC）, motion energy map

蔡鉴明，张贤贤，梁月. 道路交通场景监控视频编码研究[J]. 计算机工程与应用, 2021, 57(10): 187-193.

CAI Jianming, ZHANG Xianxian, LIANG Yue. Research on Surveillance Video Coding in Road Traffic Scene[J]. Computer Engineering and Applications, 2021, 57(10): 187-193.

[1]	王林，柴江云. 深度神经网络在多场景车辆属性识别中的研究[J]. 计算机工程与应用, 2021, 57(9): 162-167.
[2]	秦胜君，李婷. 多交互车辆轨迹预测研究[J]. 计算机工程与应用, 2021, 57(11): 232-238.
[3]	孔方方，宋蓓蓓. 改进YOLOv3的全景交通监控目标检测[J]. 计算机工程与应用, 2020, 56(8): 20-25.
[4]	陈丽丽1，沈燕飞2，邓米克1，蔡志峰3. 基于SSE2对H.265/HEVC中分像素插值的优化[J]. 计算机工程与应用, 2014, 50(19): 136-142.
[5]	田秀华. EPZS运动估计算法的分析及改进[J]. 计算机工程与应用, 2012, 48(27): 155-158.
[6]	李志敏，郭科伟，黄鸿，黄凯梁. 一种基于块分割结构的运动估计新算法[J]. 计算机工程与应用, 2012, 48(23): 143-147.
[7]	王镇波1，2，余志1，2，赵建华2，3，李熙莹1，2，罗东华4. 一种交通监控场景下的多车道检测方法[J]. 计算机工程与应用, 2012, 48(12): 14-18.
[8]	肖艳1，刘峰1，夏宏飞2. 频率域Wyner-Ziv视频编码算法的改进[J]. 计算机工程与应用, 2011, 47(12): 206-209.
[9]	李宗剑^1,2,曾理^1,3,郭燕^1,3. 基于脊波运动检测的监控视频压缩方法[J]. 计算机工程与应用, 2008, 44(14): 175-177.
[10]	王镇道¹,章兢²,曾云¹,陈迪平¹. 基于运动特征的自适应运动估计算法[J]. 计算机工程与应用, 2007, 43(31): 19-22.
[11]	王燕妮^1,2,樊养余¹. 基于视频压缩的多分辨率预测菱形搜索算法[J]. 计算机工程与应用, 2007, 43(27): 95-97.

道路交通场景监控视频编码研究

Research on Surveillance Video Coding in Road Traffic Scene

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

编辑推荐

Metrics