智能视频监控中的目标跟踪技术研究
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摘要
智能视频监控系统能够自动对异常事件进行检测和处理,符合“智慧城市”和“平安城市”的需求,近年来受到了研究人员的广泛关注。目标跟踪技术是智能视频监控系统中的核心技术之一,它是目标识别、目标分类、异常行为检测等各种高级处理技术的基础,有着非常重要的研究价值。然而,由于监控环境的动态变化(比如：光照变化、摄像机运动等)和被监控目标的动态变化(比如：姿势变化、尺度变化、相互遮挡等)使得设计鲁棒性强、实时性好的目标跟踪算法仍然是一个研究热点及难点。
本文重点研究了智能视频监控系统中的目标跟踪技术,主要针对基于静止单摄像机的单.目标跟踪算法,基于运动单摄像机的单目标跟踪算法,基于静止单摄像机的多目标跟踪算法,以及基于静止多摄像机的多目标跟踪算法等几个方面展开了相关的研究工作：
首先,本文研究了基于静止单摄像机的单目标在线跟踪算法。分别对稀疏编码和线性子空间学习进行了研究和分析,针对已有跟踪算法存在的问题,提出了两种基于单摄像机的在线目标跟踪算法：基于MLE (Maximum Likelihood Estimation)和L2范数的在线目标跟踪算法和基于特征分组的在线目标跟踪算法。与其他典型算法相比,这两种算法在目标发生遮挡、旋转、尺度变化、光照变化等异常情况时,能够比较稳定的跟踪目标,具有较强的鲁棒性。
其次,本文研究了监控摄像机处于运动状态的单目标跟踪算法。首先,筛选特征点然后,在剩余的特征点上利用光流法估计摄像机的全局运动信息,利用全局运动信息对粒子滤波算法进行修正,选择分块颜色直方图作为目标的特征模型,实现摄像机运动状态下的目标跟踪。实验结果表明,该算法能够在摄像机运动的情况下稳定的跟踪运动目标。同时,该算法克服了传统算法将稳像和跟踪分开处理的缺点,降低了计算的复杂度,提高了目标跟踪算法的处理速度。
再次,本文研究了基于静止单摄像机的多目标跟踪算法。重点研究了图割理论,以及如何将目标跟踪问题和网络图理论建立起联系。在此基础上,提出了一种基于图割的多目标跟踪算法。该算法结合像素的颜色信息和运动信息建立能量方程,构建网络图,利用最大流/最小割理论实现能量方程最小化,从而完成对多目标的跟踪。实验结果表明,本文提出的基于图割理论的多目标跟踪算法对遮挡和多目标数目的变化具有较强的鲁棒性。
最后,本文研究究了基于静止多摄像机的多目标跟踪算法。在利用码本模型进行运动目标检测的基础上,提出了两种基于多层定位的多目标跟踪算法：第一种算法利用灭点信息计算多层单应性矩阵,获取多目标的多层定位信息,利用图割理论处理多层定位信息,实现对多目标的跟踪。第二种算法利用标志物计算多层单应性矩阵,利用最短路径优化算法处理多层定位信息,进而完成多目标跟踪。实验结果表明,这两种算法对光照变化,遮挡,目标的复杂运动具有较强的鲁棒性,第二种算法能够达到实时性要求。
在鲁棒性和实时性方面,上述工作在一定程度上丰富了现有的目标跟踪算法,同时,为其在相关领域的应用进行了有益的探索。
Intelligent Video Surveillance (IVS) system can detect and process the abnormal events automaticly. It has drawn many researchers'attention as it meets the requirements of "smart city" and "safe city". As one of the core technologies in IVS. object tracking is the foundation of many advanced applications (e.g. object recognition, object classification, abnormal behavior detection, etc.), and it has very important research value. However, developing a robust and real-time tracker is still a challenging and promising problem due to the dynamic change of the surveillance environment (e.g. illumination change, camera motion, etc.) and dynamic change of the tracked objects (e.g. pose variation, scale change, occlusion, etc.).
This dissertation mainly focuses on the study of object tracking technologies in the Intelligence Video Surveillance, including the online single object tracking algorithm based on static single camera, the single object tracking algorithm based on moving single camera, the multi-object tracking algorithm based on static single camera, and the multi-object tracking algorithm based on multi-camera. The main contributions of the dissertation are summarized as follows:
Firstly, the online single-object tracking algorithm based on the single camera is studied in the dissertation. The sparse coding and linear subspace learning algorithms are introduced and analyzed. In order to overcome the existing problems of the traditional online object tracking algorithms, we present two visual object tracking algorithms:one algorithm based on the Maximum Likelihood Estimation (MLE) and L2-norm; and the other algorithm based on feature grouping. Compared with other state-of-the-art methods, the proposed methods can track the objects stably and robustly under some abnormal conditions (e.g. occlusion, rotation, scale change, illumination variation, etc.).
Secondly, the single-object tracking algorithm with cameras mounted on moving platforms is studied. Feature points are selected. And then the global motion of the camera is estimated by using optical flows on the rest feature points. To achieve robust tracking on unstable video sequences, the proposed algorithm modifies the particle filtering method according to the global motion of camera and adopts the block-based color histogram as the appearance model. Experiments demonstrate that our algorithm could track moving object robustly on challenging videos captured by moving cameras. Meanwhile, our algorithm overcomes the disadvantages of the traditional tracking algorithms that require processing video stabilization and tracking separately. Thus, it reduces the computational complexity and therefore improves the processing speed.
Thirdly, the multi-object tracking algorithm based on static single camera is studied in this dissertation. We put emphasis on introducing the graph cuts theory and discussing how to establish the relationship between object tracking and graph theory. Based on the discussions, a multi-object tracking algorithm based on the graph cuts theory is proposed. The proposed algorithm combines the color and motion information of the pixels to establish the energy function and construct the graph. Then the algorithm minimizes the energy function using the max-flow/min-cut method and therefore achieves multiple objects tracking. The experimental results demonstrate that our multi-object tracking method is robust to occlusion and variation of the number of objects.
Finally, the multi-object tracking algorithm based on static multi-camera is studied. Based on moving objects detection using the Codebook model, we propose two multi-object tracking algorithms. The first algorithm computes the homography matrix by using the information of vanishing points. Multiple objects are located at multiple planes and then tracked by using the graph cuts theory. The second algorithm computes the view-to-view homography matrix using several landmarks on different planes and performs multi-objects tracking by the shortest paths optimization algorithm. The experimental results demonstrate that our tracking methods have strong robustness to illumination change, occlusion and complex motion. In addition, the second algorithm achieves real-time performance.
In terms of robustness and real-time performance, the above-mentioned works enrich the theory of object tracking, and make some valuable exploration on the applications of the theory in the related fields.
本文重点研究了智能视频监控系统中的目标跟踪技术,主要针对基于静止单摄像机的单.目标跟踪算法,基于运动单摄像机的单目标跟踪算法,基于静止单摄像机的多目标跟踪算法,以及基于静止多摄像机的多目标跟踪算法等几个方面展开了相关的研究工作：
首先,本文研究了基于静止单摄像机的单目标在线跟踪算法。分别对稀疏编码和线性子空间学习进行了研究和分析,针对已有跟踪算法存在的问题,提出了两种基于单摄像机的在线目标跟踪算法：基于MLE (Maximum Likelihood Estimation)和L2范数的在线目标跟踪算法和基于特征分组的在线目标跟踪算法。与其他典型算法相比,这两种算法在目标发生遮挡、旋转、尺度变化、光照变化等异常情况时,能够比较稳定的跟踪目标,具有较强的鲁棒性。
其次,本文研究了监控摄像机处于运动状态的单目标跟踪算法。首先,筛选特征点然后,在剩余的特征点上利用光流法估计摄像机的全局运动信息,利用全局运动信息对粒子滤波算法进行修正,选择分块颜色直方图作为目标的特征模型,实现摄像机运动状态下的目标跟踪。实验结果表明,该算法能够在摄像机运动的情况下稳定的跟踪运动目标。同时,该算法克服了传统算法将稳像和跟踪分开处理的缺点,降低了计算的复杂度,提高了目标跟踪算法的处理速度。
再次,本文研究了基于静止单摄像机的多目标跟踪算法。重点研究了图割理论,以及如何将目标跟踪问题和网络图理论建立起联系。在此基础上,提出了一种基于图割的多目标跟踪算法。该算法结合像素的颜色信息和运动信息建立能量方程,构建网络图,利用最大流/最小割理论实现能量方程最小化,从而完成对多目标的跟踪。实验结果表明,本文提出的基于图割理论的多目标跟踪算法对遮挡和多目标数目的变化具有较强的鲁棒性。
最后,本文研究究了基于静止多摄像机的多目标跟踪算法。在利用码本模型进行运动目标检测的基础上,提出了两种基于多层定位的多目标跟踪算法：第一种算法利用灭点信息计算多层单应性矩阵,获取多目标的多层定位信息,利用图割理论处理多层定位信息,实现对多目标的跟踪。第二种算法利用标志物计算多层单应性矩阵,利用最短路径优化算法处理多层定位信息,进而完成多目标跟踪。实验结果表明,这两种算法对光照变化,遮挡,目标的复杂运动具有较强的鲁棒性,第二种算法能够达到实时性要求。
在鲁棒性和实时性方面,上述工作在一定程度上丰富了现有的目标跟踪算法,同时,为其在相关领域的应用进行了有益的探索。
Intelligent Video Surveillance (IVS) system can detect and process the abnormal events automaticly. It has drawn many researchers'attention as it meets the requirements of "smart city" and "safe city". As one of the core technologies in IVS. object tracking is the foundation of many advanced applications (e.g. object recognition, object classification, abnormal behavior detection, etc.), and it has very important research value. However, developing a robust and real-time tracker is still a challenging and promising problem due to the dynamic change of the surveillance environment (e.g. illumination change, camera motion, etc.) and dynamic change of the tracked objects (e.g. pose variation, scale change, occlusion, etc.).
This dissertation mainly focuses on the study of object tracking technologies in the Intelligence Video Surveillance, including the online single object tracking algorithm based on static single camera, the single object tracking algorithm based on moving single camera, the multi-object tracking algorithm based on static single camera, and the multi-object tracking algorithm based on multi-camera. The main contributions of the dissertation are summarized as follows:
Firstly, the online single-object tracking algorithm based on the single camera is studied in the dissertation. The sparse coding and linear subspace learning algorithms are introduced and analyzed. In order to overcome the existing problems of the traditional online object tracking algorithms, we present two visual object tracking algorithms:one algorithm based on the Maximum Likelihood Estimation (MLE) and L2-norm; and the other algorithm based on feature grouping. Compared with other state-of-the-art methods, the proposed methods can track the objects stably and robustly under some abnormal conditions (e.g. occlusion, rotation, scale change, illumination variation, etc.).
Secondly, the single-object tracking algorithm with cameras mounted on moving platforms is studied. Feature points are selected. And then the global motion of the camera is estimated by using optical flows on the rest feature points. To achieve robust tracking on unstable video sequences, the proposed algorithm modifies the particle filtering method according to the global motion of camera and adopts the block-based color histogram as the appearance model. Experiments demonstrate that our algorithm could track moving object robustly on challenging videos captured by moving cameras. Meanwhile, our algorithm overcomes the disadvantages of the traditional tracking algorithms that require processing video stabilization and tracking separately. Thus, it reduces the computational complexity and therefore improves the processing speed.
Thirdly, the multi-object tracking algorithm based on static single camera is studied in this dissertation. We put emphasis on introducing the graph cuts theory and discussing how to establish the relationship between object tracking and graph theory. Based on the discussions, a multi-object tracking algorithm based on the graph cuts theory is proposed. The proposed algorithm combines the color and motion information of the pixels to establish the energy function and construct the graph. Then the algorithm minimizes the energy function using the max-flow/min-cut method and therefore achieves multiple objects tracking. The experimental results demonstrate that our multi-object tracking method is robust to occlusion and variation of the number of objects.
Finally, the multi-object tracking algorithm based on static multi-camera is studied. Based on moving objects detection using the Codebook model, we propose two multi-object tracking algorithms. The first algorithm computes the homography matrix by using the information of vanishing points. Multiple objects are located at multiple planes and then tracked by using the graph cuts theory. The second algorithm computes the view-to-view homography matrix using several landmarks on different planes and performs multi-objects tracking by the shortest paths optimization algorithm. The experimental results demonstrate that our tracking methods have strong robustness to illumination change, occlusion and complex motion. In addition, the second algorithm achieves real-time performance.
In terms of robustness and real-time performance, the above-mentioned works enrich the theory of object tracking, and make some valuable exploration on the applications of the theory in the related fields.
引文
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