基于视频的交通冲突自动判别技术研究
发布时间：2018-12-27 13:48
【摘要】：伴随着中国进入汽车时代，交通事故也增多起来，由此带来了巨大的经济损失。因此，有效的对交叉口或路段进行交通安全性评价成为一个重要的研究课题。由于交通事故的后发性，用交通事故评价交通安全，在实时性和准确性方面稍显不足。因此，基于交通冲突的交通安全评价方法得到了广泛的研究。 虽然基于交通冲突的方法能够较好的评价交通安全性，但是目前的交通冲突的采集方法主要是通过人工观察的手段，这种方法实时性差且耗时费力，由此带来一些测量上的不便。因此本文提出了基于视频的交通冲突自动判别技术，该技术能够自动判别交通冲突的发生，，克服了基于人工观测方法的诸多弊端。 目前基于视频的交通冲突自动判别系统的研究存在着诸多问题，如目标检测不准确，跟踪效果不好，冲突判别正确率不高等。因此本文针对目前交通冲突自动判别系统研究中存在的问题，做出了深入的研究。主要工作如下： （1）基于背景差分的目标检测算法 目标检测是进行交通冲突自动判别的第一步，目标检测的准确程度直接决定着冲突判别的准确程度。本文对比了现有各个目标检测算法的优劣，最后决定采用背景差分算法提取目标。该算法首先利用背景初始化算法提取背景，并且利用背景更新模型更新背景。然后利用背景差分算法得到二值化前景图像，并用连通区域标定算法得到各个前景目标。最后，利用目标分类算法得到目标的分类，实现目标检测。通过实验分析，本算法取得了良好的检测效果。 （2）基于在线学习的目标跟踪算法 接着要进行目标跟踪，本文提出了改进的在线增强跟踪算法。由于原始的在线增强跟踪算法存在着实时性差和对左转目标漂移的问题，因此本文对其进行了改进。首先，本文提出了一个级联分类器提高跟踪速度，然后提出了一个主方向模型改进跟踪效果，解决了跟踪中存在的漂移问题，最后提出了目标位置预测模型减少搜索区域，进一步提高跟踪的实时性。通过实验分析，本文改进的在线增强跟踪算法较传统在线增强算法跟踪速度快，跟踪精度高。 （3）交通冲突自动判别算法 在获取了目标检测区域内的所有目标的轨迹序列和速度序列数据后，就要进行实时自动的交通判别。首先本文建立了基于临界距离的交通冲突判别模型，能够对交通冲突进行判别。然后基于视频处理技术，建立了交通冲突的判别流程。将传统检测方法和本文基于视频的自动判别方法进行对比实验，结果表明：本文所提出的方法判别速度更快，准确度更高。 总而言之，本文进一步深化了对自动交通冲突判别领域的研究，所提出的算法与现有方法相比更具实时性和准确性，能够为交叉口或路段安全性评价服务，具有重要的理论与实用价值。
[Abstract]:With China entering the automobile age, traffic accidents have also increased, which has brought huge economic losses. Therefore, it is an important research topic to evaluate the traffic safety of intersection or section effectively. Due to the lateness of traffic accidents, the evaluation of traffic safety by traffic accidents is a little insufficient in real time and accuracy. Therefore, traffic safety evaluation method based on traffic conflict has been widely studied. Although the method based on traffic conflict can better evaluate the traffic safety, the current traffic conflict collection method is mainly through the means of manual observation, this method is poor real-time and time-consuming. This brings some inconvenience in measurement. Therefore, this paper proposes a video based automatic discrimination technique for traffic conflicts, which can automatically distinguish the occurrence of traffic conflicts and overcome many disadvantages of artificial observation methods. At present, there are many problems in the research of traffic conflict automatic discriminant system based on video, such as inaccurate target detection, poor tracking effect, low accuracy of conflict discrimination and so on. Therefore, this paper makes an in-depth study on the existing problems in the research of traffic conflict automatic discrimination system. The main work is as follows: (1) Target detection algorithm based on background difference is the first step of automatic traffic conflict discrimination. The accuracy of target detection directly determines the accuracy of conflict discrimination. This paper compares the advantages and disadvantages of the existing target detection algorithms, and finally decides to use background differential algorithm to extract the target. Firstly, the background initialization algorithm is used to extract the background, and the background update model is used to update the background. Then the background difference algorithm is used to obtain the binary foreground image and the connected region calibration algorithm is used to obtain each foreground target. Finally, the target classification algorithm is used to achieve target detection. Through experimental analysis, the algorithm has achieved a good detection effect. (2) the target tracking algorithm based on online learning is proposed in this paper. Since the original on-line enhanced tracking algorithm has the problems of poor real-time performance and drift to the left turn target, this paper improves the algorithm. Firstly, a cascade classifier is proposed to improve the tracking speed, then a main direction model is proposed to improve the tracking effect, which solves the drift problem in tracking. Finally, the target location prediction model is proposed to reduce the search area. Further improve the real-time tracking. Through the experimental analysis, the improved on-line enhancement tracking algorithm is faster than the traditional on-line enhancement algorithm, and the tracking accuracy is high. (3) the traffic conflict automatic discriminant algorithm will carry on the real-time automatic traffic discrimination after obtaining the track sequence and velocity sequence data of all the targets in the target detection area. Firstly, a traffic conflict discriminant model based on critical distance is established, which can distinguish traffic conflict. Then, based on the video processing technology, the traffic conflict identification process is established. By comparing the traditional detection method with the automatic discriminant method based on video in this paper, the results show that the method proposed in this paper is faster and more accurate. In a word, this paper further deepens the research on the field of automatic traffic conflict discrimination. Compared with the existing methods, the proposed algorithm is more real-time and accurate, and can serve for the safety evaluation of intersections or sections. It has important theoretical and practical value.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U491.265;U495
本文编号：2393164
[Abstract]:With China entering the automobile age, traffic accidents have also increased, which has brought huge economic losses. Therefore, it is an important research topic to evaluate the traffic safety of intersection or section effectively. Due to the lateness of traffic accidents, the evaluation of traffic safety by traffic accidents is a little insufficient in real time and accuracy. Therefore, traffic safety evaluation method based on traffic conflict has been widely studied. Although the method based on traffic conflict can better evaluate the traffic safety, the current traffic conflict collection method is mainly through the means of manual observation, this method is poor real-time and time-consuming. This brings some inconvenience in measurement. Therefore, this paper proposes a video based automatic discrimination technique for traffic conflicts, which can automatically distinguish the occurrence of traffic conflicts and overcome many disadvantages of artificial observation methods. At present, there are many problems in the research of traffic conflict automatic discriminant system based on video, such as inaccurate target detection, poor tracking effect, low accuracy of conflict discrimination and so on. Therefore, this paper makes an in-depth study on the existing problems in the research of traffic conflict automatic discrimination system. The main work is as follows: (1) Target detection algorithm based on background difference is the first step of automatic traffic conflict discrimination. The accuracy of target detection directly determines the accuracy of conflict discrimination. This paper compares the advantages and disadvantages of the existing target detection algorithms, and finally decides to use background differential algorithm to extract the target. Firstly, the background initialization algorithm is used to extract the background, and the background update model is used to update the background. Then the background difference algorithm is used to obtain the binary foreground image and the connected region calibration algorithm is used to obtain each foreground target. Finally, the target classification algorithm is used to achieve target detection. Through experimental analysis, the algorithm has achieved a good detection effect. (2) the target tracking algorithm based on online learning is proposed in this paper. Since the original on-line enhanced tracking algorithm has the problems of poor real-time performance and drift to the left turn target, this paper improves the algorithm. Firstly, a cascade classifier is proposed to improve the tracking speed, then a main direction model is proposed to improve the tracking effect, which solves the drift problem in tracking. Finally, the target location prediction model is proposed to reduce the search area. Further improve the real-time tracking. Through the experimental analysis, the improved on-line enhancement tracking algorithm is faster than the traditional on-line enhancement algorithm, and the tracking accuracy is high. (3) the traffic conflict automatic discriminant algorithm will carry on the real-time automatic traffic discrimination after obtaining the track sequence and velocity sequence data of all the targets in the target detection area. Firstly, a traffic conflict discriminant model based on critical distance is established, which can distinguish traffic conflict. Then, based on the video processing technology, the traffic conflict identification process is established. By comparing the traditional detection method with the automatic discriminant method based on video in this paper, the results show that the method proposed in this paper is faster and more accurate. In a word, this paper further deepens the research on the field of automatic traffic conflict discrimination. Compared with the existing methods, the proposed algorithm is more real-time and accurate, and can serve for the safety evaluation of intersections or sections. It has important theoretical and practical value.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U491.265;U495
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