复杂交通场景中运动目标智能监控
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摘要
随着社会的进步和发展,人们的安防意识不断提高,世界各国对公共安全也越来越重视。大量的视频监控系统被用于人们日常生活和生产的各个领域。然而目前这些系统的智能性不高,对视频录像的处理能力低,利用效率差。因此,智能视频监控作为一个新兴的研究和应用方向受到学术界、产业界和管理部门的高度重视。
本文以交通监控视频为研究对象,就其智能处理中存在的问题进行深入研究。交通场景是人们最关注的公共场所,也是各国安防中重点监控对象。因此本文的研究具有较强的应用需求以及广阔的发展前景。交通场景往往复杂多变,各种干扰不断,目标在运动中可能被阴影覆盖、被其它目标遮挡,目标特征也会随其与摄像头的角度与距离的变化而发生很大变化。在这种场景下,要做到对目标的精确检测、准确跟踪和正确分类是相当有挑战性的,因此本文的研究具有较强的学术研究价值。
本文在学习计算机视觉相关理论和现有研究成果的基础上,对交通场景中目标的检测、跟踪和分类问题进行深入研究,分别提出了具有针对性的算法。最后以提出的算法为基础,设计并实现了一套智能交通视频监控系统,用于目标违规行驶的预警和指定目标的快速检索。归纳起来本文主要完成了以下几项工作：
1)提出了一种基于运动反馈的背景建模方法。传统的背景建模方法对整个场景采用统一的更新策略对背景模型进行更新。但场景中各像素的特性是不一样的,有的像素处需对背景模型进行快速更新,有的地方需要进行较慢的更新甚至不更新。本文提出将运动目标的跟踪结果反馈回来用于指导背景模型的更新。其将场景划分为四种不同类型的区域,之后在不同区域采用相应的不同的策略对背景模型进行更新。本文方法建立的模型既对背景的变化保持鲁棒性,又对前景变动保持敏感性。此外,场景类型的错误划分对本文方法性能影响比较小；且该方法计算复杂度较低,能满足实时应用的需求。
2)提出了一种基于运动反馈的前景分割方法。一般的前景分割方法对整个场景采用统一的分割策略对前景进行分割。但场景中不同像素处的特性不一样,有的地方需要较严的分割策略来抑制噪声、防止虚警,有的地方需要较松的分割策略来防止前景空洞和割裂情形的出现。本文提出将运动目标的跟踪结果反馈回来指导前景的分割。通过反馈预测出下帧中目标区域和非目标区域,在目标区域自适应地调整分割阈值,抑制前景空洞和割裂情形的出现,在非目标区域采用较严的策略来防止虚警。
3)提出了一种遮挡自适应的目标跟踪方法。在交通场景中目标间遮挡运动是不可避免的问题,解决不好将影响系统的性能。本文根据目标外接矩形的变化特性,将目标运动状态分为独立运动状态、遮挡运动状态和分裂运动状态三种。之后针对不同状态采用相应的策略对目标进行跟踪。本文提出的方法对目标部分遮挡的情形和目标完全遮挡的情形都适用,能准确检测出目标的运动状态,对目标进行准确地跟踪。
4)提出了一种场景自适应的目标分类方法。一般的方法对场景中目标进行分类时采用统一的分类规则。当目标在场景中不同位置,体现的特征发生变化时,这些方法的分类效果将受到影响。本文采用分治的策略,为场景不同位置训练不同的分类策略,之后将目标在不同位置的分类结果进行融合来给出目标的最终分类结果。本文的方法在不同的场景下都能自适应地对交通场景中三类目标进行准确分类。对目标特征随其位置变化发生变化的场景,本文方法的优势更加明显。
5)在上述几种方法的基础上,本文设计并实现了一个智能交通视频监控系统。它能对目标的违规行驶进行预警,对指定目标进行快速检索,很好地为安防工作人员服务。
With the fast social development, public safety has caught more and more attention in all the world. A large number of video surveillance systems are widely deployed in many areas. These traditional surveillance systems, however, are lack of intelligence in the sense that their major function is to record videos without understanding the recorded contents. Such systems suffer from low processing capacity and are not fully utilized. To resolve these issues, intelligent videos surveillance emerges as a new solution, and is the hot topic in academics, industries and administration agencies.
This thesis focuses on the traffic video surveillance systems and attempts to solve some problems. Traffic scenes are the most concerned places to monitor. Therefore, our research has a strong application background and the obtained results can be implemented in reality. The traffic scenes are often complicated and challenging. They usually suffer from many kinds of disturbances. The objects in such scenes may be occluded by shadows and obscured by other objects. What's more, the characteristics of object may change dramatically when the angle and distance between the object and camera change. In these traffic scenes, it is challenging to detect, track and classify objects correctly.
Based on the available theories in computer vision and the latest research results on traffic video surveillance systems, we intensively investigate object detection, tracking and classification in the traffic scenes, and propose some approaches to tackle existing problems. In the end, our proposed approaches are implemented to design and realize an intelligent traffic video surveillance system, which can be used to give warnings on object abnormality and retrieve a specified object very fast. The contributions of this thesis can be summarized as follows.
1) This thesis proposes a background modeling approach based on the feedback of the tracking results of moving objects. The traditional approaches always adopt a uniform updating strategy to update the background model of the whole scene. However, the characteristics of pixels in the scene are different. The background models of some pixels have to update as soon as possible, while the ones of other pixels should update slowly, and some even need not to update. In this thesis, we propose to utilize the feedback of tracking results for background model updating. The scene is divided into four different kinds of regions, and then different adaptive updating strategies are taken for different types of regions. The proposed approach can well deal with the trade-off between model robustness to background changes and model sensitivity to foreground abnormalities. Moreover, the misclassification of the type of region nearly has no harm to the performance. And the proposed approach has a low complexity, which can meet the needs of real-time application.
2) This thesis proposes a foreground segmentation approach based on the feedback of the tracking results of moving objects. The general approaches always adopt a uniform segmentation strategy for the whole scene. However, the characteristics of pixels in the scene are different. In some pixel regions, it has to adopt stringent strategy for foreground segmentation so as to suppress noise, reduce false alarms. While in other pixel regions, a loose strategy should be adopted to reduce erroneous holes and splitting of objects. In this paper, we propose to feed back the tracking results for foreground segmentation. Based on the feedback information, it predicts the object regions in the following frame. In the object regions, adaptive segmentation thresholds are taken for foreground segmentation to reduce erroneous holes and splitting. In the non-object regions, a large threshold is taken for foreground segmentation to reduce false alarms.
3) This thesis proposes an occlusion-adaptive multi-object tracking approach. In the traffic scenes, the occlusion among objects and the change of objects'appearance are inevitable. If we can't well deal with these problems, it will greatly degrade the object tracking performance. According to the spatial-temporal continuity, we divide object motion into three states, including the independence state, the occluded state and the splitting state. And then three different tracking strategies are implemented for corresponding states. The proposed approach can correctly detect the objects'motion state and track the objects, both in the cases of partial occlusion and total occlusion.
4) This thesis proposes an adaptive approach for object classification. The general approaches adopt a uniform rule for object classification and can not work well when the characteristics of the objects change with their movement in the scene. Our new approach takes the divide and conquer technique. First, it trains different classification rules for different regions in the scene, and then gives the final decision of the object category by fusing the decision of the object in different regions. Experiments show that the proposed approach can well distinguish pedestrians, bicycles and cars.
5) The above proposed approaches are integrated to design and realize an intelligent traffic video surveillance system. It gives warnings on object abnormality and retrieves a specified object very efficiently.
本文以交通监控视频为研究对象,就其智能处理中存在的问题进行深入研究。交通场景是人们最关注的公共场所,也是各国安防中重点监控对象。因此本文的研究具有较强的应用需求以及广阔的发展前景。交通场景往往复杂多变,各种干扰不断,目标在运动中可能被阴影覆盖、被其它目标遮挡,目标特征也会随其与摄像头的角度与距离的变化而发生很大变化。在这种场景下,要做到对目标的精确检测、准确跟踪和正确分类是相当有挑战性的,因此本文的研究具有较强的学术研究价值。
本文在学习计算机视觉相关理论和现有研究成果的基础上,对交通场景中目标的检测、跟踪和分类问题进行深入研究,分别提出了具有针对性的算法。最后以提出的算法为基础,设计并实现了一套智能交通视频监控系统,用于目标违规行驶的预警和指定目标的快速检索。归纳起来本文主要完成了以下几项工作：
1)提出了一种基于运动反馈的背景建模方法。传统的背景建模方法对整个场景采用统一的更新策略对背景模型进行更新。但场景中各像素的特性是不一样的,有的像素处需对背景模型进行快速更新,有的地方需要进行较慢的更新甚至不更新。本文提出将运动目标的跟踪结果反馈回来用于指导背景模型的更新。其将场景划分为四种不同类型的区域,之后在不同区域采用相应的不同的策略对背景模型进行更新。本文方法建立的模型既对背景的变化保持鲁棒性,又对前景变动保持敏感性。此外,场景类型的错误划分对本文方法性能影响比较小；且该方法计算复杂度较低,能满足实时应用的需求。
2)提出了一种基于运动反馈的前景分割方法。一般的前景分割方法对整个场景采用统一的分割策略对前景进行分割。但场景中不同像素处的特性不一样,有的地方需要较严的分割策略来抑制噪声、防止虚警,有的地方需要较松的分割策略来防止前景空洞和割裂情形的出现。本文提出将运动目标的跟踪结果反馈回来指导前景的分割。通过反馈预测出下帧中目标区域和非目标区域,在目标区域自适应地调整分割阈值,抑制前景空洞和割裂情形的出现,在非目标区域采用较严的策略来防止虚警。
3)提出了一种遮挡自适应的目标跟踪方法。在交通场景中目标间遮挡运动是不可避免的问题,解决不好将影响系统的性能。本文根据目标外接矩形的变化特性,将目标运动状态分为独立运动状态、遮挡运动状态和分裂运动状态三种。之后针对不同状态采用相应的策略对目标进行跟踪。本文提出的方法对目标部分遮挡的情形和目标完全遮挡的情形都适用,能准确检测出目标的运动状态,对目标进行准确地跟踪。
4)提出了一种场景自适应的目标分类方法。一般的方法对场景中目标进行分类时采用统一的分类规则。当目标在场景中不同位置,体现的特征发生变化时,这些方法的分类效果将受到影响。本文采用分治的策略,为场景不同位置训练不同的分类策略,之后将目标在不同位置的分类结果进行融合来给出目标的最终分类结果。本文的方法在不同的场景下都能自适应地对交通场景中三类目标进行准确分类。对目标特征随其位置变化发生变化的场景,本文方法的优势更加明显。
5)在上述几种方法的基础上,本文设计并实现了一个智能交通视频监控系统。它能对目标的违规行驶进行预警,对指定目标进行快速检索,很好地为安防工作人员服务。
With the fast social development, public safety has caught more and more attention in all the world. A large number of video surveillance systems are widely deployed in many areas. These traditional surveillance systems, however, are lack of intelligence in the sense that their major function is to record videos without understanding the recorded contents. Such systems suffer from low processing capacity and are not fully utilized. To resolve these issues, intelligent videos surveillance emerges as a new solution, and is the hot topic in academics, industries and administration agencies.
This thesis focuses on the traffic video surveillance systems and attempts to solve some problems. Traffic scenes are the most concerned places to monitor. Therefore, our research has a strong application background and the obtained results can be implemented in reality. The traffic scenes are often complicated and challenging. They usually suffer from many kinds of disturbances. The objects in such scenes may be occluded by shadows and obscured by other objects. What's more, the characteristics of object may change dramatically when the angle and distance between the object and camera change. In these traffic scenes, it is challenging to detect, track and classify objects correctly.
Based on the available theories in computer vision and the latest research results on traffic video surveillance systems, we intensively investigate object detection, tracking and classification in the traffic scenes, and propose some approaches to tackle existing problems. In the end, our proposed approaches are implemented to design and realize an intelligent traffic video surveillance system, which can be used to give warnings on object abnormality and retrieve a specified object very fast. The contributions of this thesis can be summarized as follows.
1) This thesis proposes a background modeling approach based on the feedback of the tracking results of moving objects. The traditional approaches always adopt a uniform updating strategy to update the background model of the whole scene. However, the characteristics of pixels in the scene are different. The background models of some pixels have to update as soon as possible, while the ones of other pixels should update slowly, and some even need not to update. In this thesis, we propose to utilize the feedback of tracking results for background model updating. The scene is divided into four different kinds of regions, and then different adaptive updating strategies are taken for different types of regions. The proposed approach can well deal with the trade-off between model robustness to background changes and model sensitivity to foreground abnormalities. Moreover, the misclassification of the type of region nearly has no harm to the performance. And the proposed approach has a low complexity, which can meet the needs of real-time application.
2) This thesis proposes a foreground segmentation approach based on the feedback of the tracking results of moving objects. The general approaches always adopt a uniform segmentation strategy for the whole scene. However, the characteristics of pixels in the scene are different. In some pixel regions, it has to adopt stringent strategy for foreground segmentation so as to suppress noise, reduce false alarms. While in other pixel regions, a loose strategy should be adopted to reduce erroneous holes and splitting of objects. In this paper, we propose to feed back the tracking results for foreground segmentation. Based on the feedback information, it predicts the object regions in the following frame. In the object regions, adaptive segmentation thresholds are taken for foreground segmentation to reduce erroneous holes and splitting. In the non-object regions, a large threshold is taken for foreground segmentation to reduce false alarms.
3) This thesis proposes an occlusion-adaptive multi-object tracking approach. In the traffic scenes, the occlusion among objects and the change of objects'appearance are inevitable. If we can't well deal with these problems, it will greatly degrade the object tracking performance. According to the spatial-temporal continuity, we divide object motion into three states, including the independence state, the occluded state and the splitting state. And then three different tracking strategies are implemented for corresponding states. The proposed approach can correctly detect the objects'motion state and track the objects, both in the cases of partial occlusion and total occlusion.
4) This thesis proposes an adaptive approach for object classification. The general approaches adopt a uniform rule for object classification and can not work well when the characteristics of the objects change with their movement in the scene. Our new approach takes the divide and conquer technique. First, it trains different classification rules for different regions in the scene, and then gives the final decision of the object category by fusing the decision of the object in different regions. Experiments show that the proposed approach can well distinguish pedestrians, bicycles and cars.
5) The above proposed approaches are integrated to design and realize an intelligent traffic video surveillance system. It gives warnings on object abnormality and retrieves a specified object very efficiently.
引文
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