基于交通视频分析的自适应摄像控制系统

张洪斌; 黄山; 印月

doi:10.3969/j.issn.0258-2724.2018.03.028

基于交通视频分析的自适应摄像控制系统

doi: 10.3969/j.issn.0258-2724.2018.03.028

张洪斌^1,,
黄山^1,2,
印月²

基金项目:

科技部科技型中小企业技术创新基金资助项目 09C26215102248

四川省重点技术创新项目 07XM020

四川省重点技术创新项目 2014CG053

详细信息

作者简介:
张洪斌(1982-), 男, 博士研究生, 研究方向为计算机视觉在智能交通中的应用, E-mail:zhb593@163.com

中图分类号: TP391.4
计量
- 文章访问数: 402
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- PDF下载量: 87
- 被引次数: 0
出版历程
- 收稿日期: 2017-12-21
- 刊出日期: 2018-06-01

Adaptive Imaging Control System Based on Traffic Video Analysis

摘要

摘要: 为在高动态光照和复杂路况环境中实现准确的视频检测与数据采集，构建了一套有效的自适应交通摄像控制系统.首先，针对全天候大场景综合检测需求设计高清交通摄像系统，并通过系统辨识确定了核心摄像参数的控制特性；接着，基于车辆号牌和交通场景特点，提出了以车牌亮度中点值和标线区块中点值作为反馈控制指标的视频质量分析算法；最后，使用自适应控制架构综合底层图像质量反馈信息和高层视觉分析检测结果，实现了光照模式的感知适应和控制状态的自主切换.实验与应用结果表明：该系统控制过程快速稳定，能够自动适应不同光照环境，兼顾高清晰车牌识别和大视野交通监控需求，保持良好的全天成像效果，实现了平均97.0%的车流准确度和96.3%的车牌识别率.
- 成像系统 /
- 图像分析 /
- 自适应控制 /
- 车辆检测 /
- 车牌识别 /
- 智能交通系统
Abstract: To ensure accurate video detection and data collection under high dynamic illumination and complicated road conditions, an adaptive traffic imaging control system was developed. A high-definition traffic imaging system was designed to meet the needs of full-time wide-field comprehensive detection. The control characteristics of the imaging parameters were acquired through system identification. Based on license-plate properties and traffic scenes, a video-analysis algorithm computing mid-values of license-plate images and road-marking blocks as feedback control variables, was proposed. In an adaptive control framework combining low-level image-quality feedback and high-level visual-detection results, autonomous illumination-mode adaption and control-state switching were realized. The experimental and application results show that the system control process is fast and stable. It can adapt to different illumination conditions, balance the requirements of high-definition license-plate recognition and wide-field traffic surveillance, maintain good all-day imaging effects, and achieve 97.0% traffic-flow accuracy and a 96.3% license-plate recognition rate.
- imaging systems /
- image analysis /
- adaptive control systems /
- vehicle detection /
- license plate recognition /
- intelligent transport system

HTML全文

图 1 高清交通摄像系统

Figure 1. High-definition traffic-imaging system

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图 2 摄像系统延时统计

Figure 2. Imaging-system time-lag statistical graph

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图 3 摄像参数控制特性

Figure 3. Control characteristics of imaging parameters

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图 4 计算车牌亮度中点值

Figure 4. Computation of license plate mid-value

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图 5 车牌亮度中点值的分区

Figure 5. Partition of license plate mid-value range

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图 6 交通场景中的标线区块

Figure 6. Road-marking blocks of a traffic scene

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图 7 标线区块的可见性判别

Figure 7. Visibility detection of road-marking blocks

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图 8 自适应摄像控制系统

Figure 8. Adaptive imaging control-system graph

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图 9 光照模式的感知与控制

Figure 9. Detection and control of different illumination modes

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图 10 控制状态转换图

Figure 10. Control state-transition graph

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图 11 光照扰动下的摄像控制效果

Figure 11. Imaging control results with illumination changes

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图 12 逆光成像效果对比

Figure 12. Imaging effects comparison of backlighting

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图 13 顺光成像效果对比

Figure 13. Imaging effects comparison of front-lighting

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图 14 摄像控制量24 h曲线

Figure 14. Twenty-four-hour curves of imaging parameters

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表 1 特殊光照检测结果

Table 1. Detection results under special illumination conditions

%
摄像控制算法	车牌识别率		场景完整率
摄像控制算法	逆光	顺光	逆光	顺光
算法1	54.8	33.2	96.4	98.0
算法2	94.4	93.2	55.6	86.2
本方法	96.6	96.0	97.8	98.8

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表 2 典型时段成像检测效果

Table 2. Imaging and detection results at typical moments

观测统计项	黎明 7:00—7:30	上午 10:00—10:30	正午 12:00—12:30	下午 15:30—16:00	黄昏 18:30—19:00	子夜 23:30—24:00
车牌图像
路面 T形区块
场景图像
实际过车数/辆	352	1011	816	937	1507	276
车流准确率/%	97.2	97.5	99.1	98.3	94.8	96.7
车牌识别率/%	95.1	98.1	97.2	98.4	94.2	92.1

下载: 导出CSV

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