复杂环境下基于图像特征的交通事件检测算法研究

发布时间：2018-06-26 06:38

本文选题：智能交通 + 交通事件检测　；参考：《华南理工大学》2015年硕士论文

【摘要】：视频交通事件检测系统是利用图像处理技术,结合人工智能和机器学习等学科知识对高速公路监控视频进行分析,自动检测出高速公路上出现的异常事件的系统。它能够快速、准确的检测出高速公路运营过程中出现的异常事件,同时向高速公路管理人员发出相应信息,将异常事件对高速公路运营的影响降到最低,确保高速公路畅通运行。它是高速公路应急管理平台及智能交通系统中不可或缺的前端信息采集子系统。随着我国高速公路拥堵问题日益严重,视频交通事件检测系统正逐步得到广泛的应用,因此关于此技术的研究就显得非常有意义。结合高速公路监控系统现状,本文对目前监控系统中存在的问题进行分析,拟解决几个当前视频检测系统中普遍存在的问题,着重提高视频检测系统的检测率和环境稳定性。本文首先解决的问题就是提高监控系统的环境适应能力。由于高速公路监控大多属于露天环境,监控环境复杂多变,对监控系统的环境适应能力有较高的要求,通过对多条高速公路现场环境分析,采集数据,本文提出了一种新的分类算法对日夜切换、雨天路面积水、夜间雨天路灯反光等恶劣环境进行识别。其次,利用背景差分技术对高速公路中存在的交通目标进行目标检测。在背景差分二值化目标检测技术中,提出了一种新的自适应背景差分阈值确定算法,以适应高速公路复杂多变的监控环境。同时针对恶劣监控环境采用有针对性的识别算法以保证系统检测的准确性和稳定性。最后,利用基于目标跟踪检测的事件检测系统对获取的目标进行跟踪检测分析。构建了适应于高速公路监控环境运动目标跟踪的Kalman预估器,分析各类异常交通事件中运动目标特征或者监控画面的特点,通过机器学习算法对各类常见的交通事件的特征参数进行定义及阈值处理,实现了各类异常交通事件的准确检测。通过在国内某几条高速公路进行的模拟测试验证了本文提出的交通事件检测算法具有很好的性能,在多种复杂环境下均能准确检测出各类常见异常事件。
[Abstract]:Video traffic event detection system is a system that uses image processing technology, combining with artificial intelligence and machine learning to analyze the video of highway surveillance, and automatically detects the abnormal events on the highway. It can detect the abnormal events in the expressway operation process quickly and accurately, at the same time, it can send the corresponding information to the expressway management personnel to minimize the impact of the abnormal events on the expressway operation. Make sure the freeway runs smoothly. It is an indispensable front-end information collection subsystem in expressway emergency management platform and intelligent transportation system. With the increasingly serious problem of highway congestion in China, video traffic incident detection system is gradually being widely used, so the research on this technology is very meaningful. Combined with the current situation of highway monitoring system, this paper analyzes the existing problems in the current monitoring system, and proposes to solve several common problems in the current video detection system, focusing on improving the detection rate and environmental stability of the video detection system. The first problem to be solved in this paper is to improve the environmental adaptability of the monitoring system. Because the expressway monitoring mostly belongs to the open-air environment, the monitoring environment is complex and changeable, and has higher requirements for the environmental adaptability of the monitoring system. In this paper, a new classification algorithm is proposed to identify the bad environment, such as day and night switching, rainy day road surface water accumulation, night rainy day street lamp reflection and so on. Secondly, the background differential technique is used to detect the traffic targets in expressway. In the background differential binary target detection technology, a new adaptive background differential threshold determination algorithm is proposed to adapt to the complicated and changeable monitoring environment of freeway. In order to ensure the accuracy and stability of the detection system, a targeted recognition algorithm is adopted for the adverse monitoring environment at the same time. Finally, the event detection system based on target tracking detection is used to track and detect the acquired target. A Kalman predictor suitable for tracking moving targets in highway monitoring environment is constructed to analyze the characteristics of moving targets or monitoring pictures in various kinds of abnormal traffic events. By means of machine learning algorithm, the characteristic parameters of various common traffic events are defined and the threshold value is processed to realize the accurate detection of all kinds of abnormal traffic events. The traffic event detection algorithm proposed in this paper is proved to have good performance by simulating tests on some domestic freeways. It can accurately detect all kinds of common abnormal events in various complex environments.
【学位授予单位】：华南理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U495;TP391.41

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