智能交通系统中基于视频的行人检测与跟踪方法的研究
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摘要
在智能交通和计算机视觉领域中,行人的检测和跟踪是一个重要的,也是最基本的任务,是视频场景分析、高层语义分析等诸多后续工作的基础。行人检测与跟踪技术在视频监控、交通、人机交互、机器人视觉导航、虚拟现实、医学图像和国防等各方面都有着广泛的应用。尽管行人的检测和跟踪技术已经研究了十多年,但仍是一个非常活跃的研究领域,目前还没有一个标准的,健壮的,精确的,高性能的,和实时的行人检测和跟踪算法。由于行人固有的一些特性,应用场景的复杂性,人与人或人与环境之间的相互影响,使得行人的检测和跟踪是计算机视觉研究领域中最难的一项挑战。本论文研究了视频中单摄像机下的行人检测与跟踪的几个关键问题,包括目标与背景、阴影的准确分割;准确的行人判断;更快的跟踪速度;更准确的跟踪定位。论文的重点及创新成果包括:
(1)前景与背景的分割
本文对视频中运动物体检测进行了研究,针对现有运动物体检测方法的不足之处,提出了一种鲁棒性较好的,能满足实时性要求的区间分布式背景模型的目标检测算法,主要包括预处理,区间分布模型建模、降噪处理、前景提取和背景更新等步骤。该方法的基础是本文提出的区间分布模型,该模型旨在建立一个快速,准确,具有较强适应性的背景模型;为了更好的适应光照、天气等的变化,该算法包含适时的背景更新策略。
(2)阴影的检测
对视频目标检测中的阴影检测问题进行了分析,提出了一种基于Gabor小波和颜色模型的阴影检测算法。首先,建立背景的区间分布模型和阴影参数模型,通过差分法提取前景区域并结合Gabor小波纹理特征分析找出潜在的阴影点;然后通过阴影颜色模型对这些潜在的阴影点进行颜色分析,找出真正的阴影区域。
(3)静态背景下的行人检测
针对静止摄像头下行人检测存在的问题进行了研究,提出了一种基于混合特征集的行人检测方法,该方法采用本文提出的混合特征集。首先,本文在借鉴矩形特征的边缘描述方法,分析行人姿态的边缘特性后得到了新的特征集-三角特征集,在融合矩形特征、三角特征和非对称特征之后,本文提出了一种新的特征集-混合特征集。同时,针对传统的AdaBoost算法(基于矩形特征集的AdaBoost算法)中存在过度拟和,本文提出了一种改进的AdaBoost算法,该方法使用的是混合特征集,并优化了阀值选取策略、权重更新策略和归一化处理,改进了原算法的样本训练过程,最后针对视频中行人随着镜头距离变化而导致大小变化的特点,提出了多尺度窗口遍历策略。
(4)动态背景下的行人检测
针对移动摄像头下的行人检测进行了研究,提出了一种基于量子演化的行人检测优化方法。该方法建立在AdaBoost行人分类算法、支持向量机(SVM)理论和多目标优化原理的基础之上,该方法的核心是基于实值编码的量子演化算法。首先,使用AdaBoost算法对行人进行粗粒度的分类,然后使用支持向量机(SVM)设计精度更高的行人检测器。针对SVM的分类器参数多,关系复杂,而且无好的调节准则,本文根据核函数的构建条件,将实值量子演化算法引入到SVM参数的寻优问题中,对于分类性能采用多目标优化的概念,取得了较好的效果,同时从理论上分析了算法的复杂度,保证了算法的实时性。
(5)行人跟踪
提出了一种改进的CamShift算法和关联矩阵相结合的行人跟踪算法。本文首先提出了一种改进的CamShift的定位算法,该算法用来对行人目标进行精确定位。为了解决复杂情况下跟踪问题,如行人目标的聚集,遮挡,突然消失,出现等情况,在此基础之上,提出了一种基于CamShift和多因素关联矩阵的算法。首先通过区间分布模型和阴影检测,获取前景团块,然后通过Kalman滤波对目标位置进行预测,用改进的CamShift定位算法在预测位置的基础之上对行人目标实现精准定位,接着分析前景团块与预测的目标的相似度,建立前景团块与目标之间的多因素关联矩阵,经过关联矩阵的判断和推理,完成对行人在复杂环境下的准确跟踪。
Visual pedestrian detection and tracking is a key problem in computer vision and ITS, and it is the basis of follow-up treatment, such as visual scene analysis and semantic analysis. The technique of visual pedestrian detection and tracking has a wide range of applications in intelligent video surveillance, traffic, human-computer interaction, visual navigation of robots, virtual reality, medical image processing, national defense, etc. Although the pedestrian detection and tracking has been studied for more than ten years in computer vision community, it is still an active research area. In present, there is no pedestrian detection and tracking system which is general, robust, accurate, efficient, and real time. Because the human body is non-rigid, the scenes are cluttered, and there are a lot of interactions among the pedestrians or between the pedestrians and the scenes, the human detection and tracking is one of the most difficult challenges in the area of computer vision research.
In this dissertation, some key issues of pedestrian detection and tracking have been discussed, including accurate segmentation of background, objects and shadows, accurate pedestrian recognition, faster speed of tracking, more accurate location and pedestrian’s activity understanding and description. The highlights and main contributions of the dissertation include:
(1) Accurate segmentation of background and objects
This paper mainly does the research on moving object detection in video sequences. To point against the shortcomings of the existing moving object detection algorithms, this paper proposed a kind of more robust and better qualified to real time request object detection algorithm, and section-distribution background model. The object detection algorithm includes pre-process, section-distribution modeling, denoising, foreground extraction and background update and so on. As the basis of this approach, the section distribution model aimed to establish a rapid, accurate, and strong adaptive background model. In order to better adapt the changes brought by illumination, weather or other factors better, the algorithm contains timely background update strategy.
(2) Accurate segmentation of shadows
A new shadow detection algorithm based on the Gabor wavelet and the color model is proposed in this paper. Firstly, the Gaussian mixture distribution model and shadow color model of the background are established. Next, the foreground figures are extracted by means of the difference method. Then, the potential shadow pixels are found out by means of Gabor wavelet texture analysis, which are further analyzed with the shadow color model to search real shadow pixels. Finally, the real shadow areas are distinguished.
(3) Pedestrian detection under static background
This paper does a research on pedestrian detection issue in video, and presentes a pedestrian detection approach based on triangle feature set, which adopted the proposed triangle feature set. Firstly, this paper uses the rectangle feature for reference to describe the edge feature, through analyzing the edge feature of the pedestrian posture to obtain new feature set—triangle feature set. Secondly by mixed rectangle feature, triangle feature and asymmetrical feature together, this paper raises a new feature set - Hybrid feature set. Meanwhile, to point against the conventional Adaboost algorithm (Adaboost algorithm based on rectangle feature set) which exists overfitting, an improved Adaboost algorithm has been presented. The algorithm utilized the hybrid feature set, and optimized the threshold selection strategy, the weight update strategy and the normalized process,and improved the sample training procedure of the primary algorithm. At last, considering the feature that the size of pedestrian in video would change with the different distance between the camera and the pedestrian, this paper presentes a multi-scale window traverse strategy.
(4) Pedestrian detection against camera shift
Study on pedestrian detection against camera shift, this paper proposes proposed an optimization pedestrian detection algorithm based on quantum evolution. This approach bases on AdaBoost pedestrian detection algorithm, supporting vector machine (SVM) and multi-objective optimization theory as the basis, and the core of the approach is quantum evolution which bases on real encoding. Firstly, it utilizes the AdaBoost to classify pedestrian with coarse granularity, and then employ SVM to design more accurate pedestrian detector. Taking multi-parameter with complex relationships and no reasonable regulation criteria into account, this paper considers the construction condition of kernel function, introduce real quantum evolution algorithm to the domain of SVM parameter optimization problems, and adopts multi-objective optimization concept to enhance the classification performance, which achieves good results. Meanwhile the complexity of the algorithm has been analyzed in theory to ensure the real-time characteristic.
(5) Pedestrian tracking
This paper proposes a pedestrian tracking algorithm by using improved CamShift algorithm and incidence matrix. Firstly, a position algorithm is presented by an improved CamShift, which has been used to make accurate position on pedestrian object. To solve the tracking problems under complex scenarios, including the convergence, occlusion, sudden disappear and appear of the pedestrian objects and so on, this paper proposes an algorithm based on multi-factor incidence matrix. Firstly, section distribution model and shadow detection has been used to acquire foreground blob. Secondly, prediction of object position has been realized by using Kalman filter. Thirdly, the improved CamShift algorithm has been used to realize accurate position. Fourthly, the similarity between the foreground blob and the predicted object has been analyzed as well. Then the multi-factor incidence matrix between foreground blob and object has been setup. Finally the pedestrian accurate tracking under complex scenarios has been completed by judging and reasoning according the incidence matrix.
(1)前景与背景的分割
本文对视频中运动物体检测进行了研究,针对现有运动物体检测方法的不足之处,提出了一种鲁棒性较好的,能满足实时性要求的区间分布式背景模型的目标检测算法,主要包括预处理,区间分布模型建模、降噪处理、前景提取和背景更新等步骤。该方法的基础是本文提出的区间分布模型,该模型旨在建立一个快速,准确,具有较强适应性的背景模型;为了更好的适应光照、天气等的变化,该算法包含适时的背景更新策略。
(2)阴影的检测
对视频目标检测中的阴影检测问题进行了分析,提出了一种基于Gabor小波和颜色模型的阴影检测算法。首先,建立背景的区间分布模型和阴影参数模型,通过差分法提取前景区域并结合Gabor小波纹理特征分析找出潜在的阴影点;然后通过阴影颜色模型对这些潜在的阴影点进行颜色分析,找出真正的阴影区域。
(3)静态背景下的行人检测
针对静止摄像头下行人检测存在的问题进行了研究,提出了一种基于混合特征集的行人检测方法,该方法采用本文提出的混合特征集。首先,本文在借鉴矩形特征的边缘描述方法,分析行人姿态的边缘特性后得到了新的特征集-三角特征集,在融合矩形特征、三角特征和非对称特征之后,本文提出了一种新的特征集-混合特征集。同时,针对传统的AdaBoost算法(基于矩形特征集的AdaBoost算法)中存在过度拟和,本文提出了一种改进的AdaBoost算法,该方法使用的是混合特征集,并优化了阀值选取策略、权重更新策略和归一化处理,改进了原算法的样本训练过程,最后针对视频中行人随着镜头距离变化而导致大小变化的特点,提出了多尺度窗口遍历策略。
(4)动态背景下的行人检测
针对移动摄像头下的行人检测进行了研究,提出了一种基于量子演化的行人检测优化方法。该方法建立在AdaBoost行人分类算法、支持向量机(SVM)理论和多目标优化原理的基础之上,该方法的核心是基于实值编码的量子演化算法。首先,使用AdaBoost算法对行人进行粗粒度的分类,然后使用支持向量机(SVM)设计精度更高的行人检测器。针对SVM的分类器参数多,关系复杂,而且无好的调节准则,本文根据核函数的构建条件,将实值量子演化算法引入到SVM参数的寻优问题中,对于分类性能采用多目标优化的概念,取得了较好的效果,同时从理论上分析了算法的复杂度,保证了算法的实时性。
(5)行人跟踪
提出了一种改进的CamShift算法和关联矩阵相结合的行人跟踪算法。本文首先提出了一种改进的CamShift的定位算法,该算法用来对行人目标进行精确定位。为了解决复杂情况下跟踪问题,如行人目标的聚集,遮挡,突然消失,出现等情况,在此基础之上,提出了一种基于CamShift和多因素关联矩阵的算法。首先通过区间分布模型和阴影检测,获取前景团块,然后通过Kalman滤波对目标位置进行预测,用改进的CamShift定位算法在预测位置的基础之上对行人目标实现精准定位,接着分析前景团块与预测的目标的相似度,建立前景团块与目标之间的多因素关联矩阵,经过关联矩阵的判断和推理,完成对行人在复杂环境下的准确跟踪。
Visual pedestrian detection and tracking is a key problem in computer vision and ITS, and it is the basis of follow-up treatment, such as visual scene analysis and semantic analysis. The technique of visual pedestrian detection and tracking has a wide range of applications in intelligent video surveillance, traffic, human-computer interaction, visual navigation of robots, virtual reality, medical image processing, national defense, etc. Although the pedestrian detection and tracking has been studied for more than ten years in computer vision community, it is still an active research area. In present, there is no pedestrian detection and tracking system which is general, robust, accurate, efficient, and real time. Because the human body is non-rigid, the scenes are cluttered, and there are a lot of interactions among the pedestrians or between the pedestrians and the scenes, the human detection and tracking is one of the most difficult challenges in the area of computer vision research.
In this dissertation, some key issues of pedestrian detection and tracking have been discussed, including accurate segmentation of background, objects and shadows, accurate pedestrian recognition, faster speed of tracking, more accurate location and pedestrian’s activity understanding and description. The highlights and main contributions of the dissertation include:
(1) Accurate segmentation of background and objects
This paper mainly does the research on moving object detection in video sequences. To point against the shortcomings of the existing moving object detection algorithms, this paper proposed a kind of more robust and better qualified to real time request object detection algorithm, and section-distribution background model. The object detection algorithm includes pre-process, section-distribution modeling, denoising, foreground extraction and background update and so on. As the basis of this approach, the section distribution model aimed to establish a rapid, accurate, and strong adaptive background model. In order to better adapt the changes brought by illumination, weather or other factors better, the algorithm contains timely background update strategy.
(2) Accurate segmentation of shadows
A new shadow detection algorithm based on the Gabor wavelet and the color model is proposed in this paper. Firstly, the Gaussian mixture distribution model and shadow color model of the background are established. Next, the foreground figures are extracted by means of the difference method. Then, the potential shadow pixels are found out by means of Gabor wavelet texture analysis, which are further analyzed with the shadow color model to search real shadow pixels. Finally, the real shadow areas are distinguished.
(3) Pedestrian detection under static background
This paper does a research on pedestrian detection issue in video, and presentes a pedestrian detection approach based on triangle feature set, which adopted the proposed triangle feature set. Firstly, this paper uses the rectangle feature for reference to describe the edge feature, through analyzing the edge feature of the pedestrian posture to obtain new feature set—triangle feature set. Secondly by mixed rectangle feature, triangle feature and asymmetrical feature together, this paper raises a new feature set - Hybrid feature set. Meanwhile, to point against the conventional Adaboost algorithm (Adaboost algorithm based on rectangle feature set) which exists overfitting, an improved Adaboost algorithm has been presented. The algorithm utilized the hybrid feature set, and optimized the threshold selection strategy, the weight update strategy and the normalized process,and improved the sample training procedure of the primary algorithm. At last, considering the feature that the size of pedestrian in video would change with the different distance between the camera and the pedestrian, this paper presentes a multi-scale window traverse strategy.
(4) Pedestrian detection against camera shift
Study on pedestrian detection against camera shift, this paper proposes proposed an optimization pedestrian detection algorithm based on quantum evolution. This approach bases on AdaBoost pedestrian detection algorithm, supporting vector machine (SVM) and multi-objective optimization theory as the basis, and the core of the approach is quantum evolution which bases on real encoding. Firstly, it utilizes the AdaBoost to classify pedestrian with coarse granularity, and then employ SVM to design more accurate pedestrian detector. Taking multi-parameter with complex relationships and no reasonable regulation criteria into account, this paper considers the construction condition of kernel function, introduce real quantum evolution algorithm to the domain of SVM parameter optimization problems, and adopts multi-objective optimization concept to enhance the classification performance, which achieves good results. Meanwhile the complexity of the algorithm has been analyzed in theory to ensure the real-time characteristic.
(5) Pedestrian tracking
This paper proposes a pedestrian tracking algorithm by using improved CamShift algorithm and incidence matrix. Firstly, a position algorithm is presented by an improved CamShift, which has been used to make accurate position on pedestrian object. To solve the tracking problems under complex scenarios, including the convergence, occlusion, sudden disappear and appear of the pedestrian objects and so on, this paper proposes an algorithm based on multi-factor incidence matrix. Firstly, section distribution model and shadow detection has been used to acquire foreground blob. Secondly, prediction of object position has been realized by using Kalman filter. Thirdly, the improved CamShift algorithm has been used to realize accurate position. Fourthly, the similarity between the foreground blob and the predicted object has been analyzed as well. Then the multi-factor incidence matrix between foreground blob and object has been setup. Finally the pedestrian accurate tracking under complex scenarios has been completed by judging and reasoning according the incidence matrix.
引文
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