目标跟踪系统中的鲁棒性研究
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摘要
随着计算机科学、电子技术、人工智能等的发展与普及,目标跟踪技术已广泛地应用到民用和军事上,如一些重要场所的视频监控系统、自主导航、智能交通监控系统、人机交互系统、视频压缩等。近年来,大量研究人员对目标跟踪进行了广泛、深入的研究,并针对各种应用环境提出了有效的视频目标跟踪算法。然而,由于目标跟踪系统相当复杂,故研究具有鲁棒性强、实用性好的目标跟踪算法仍然是计算机视觉领域的研究热点之一。
本文对目标跟踪系统中的目标特征选择、目标表示模型、相似性度量、目标定位算法这四个方面开展了深入研究,提出了一些新方法。论文的主要研究内容和成果概括如下：
1.当背景与目标的颜色分布比较相似时,CAMSHIFT目标跟踪算法就不能成功跟踪目标,为此,本文提出了一种基于自适应高斯混合模型的目标稳定分布提取算法。该算法首先用一种快速有效的自适应高斯混合模型建模方法对目标和背景建模,用改进的巴氏距离评估目标模型中所有高斯单元的区分能力,选择出具有较高区分能力的高斯单元,并由它们生成目标的稳定分布。实验结果表明该算法能够成功提取目标的稳定分布,并能找到目标中的稳定部分,将目标稳定分布应用到CAMSHIFT算法后能成功跟踪目标,从而提高了目标跟踪性能。
2.现有的基于距离度量的目标特征选择方法大多只适合度量两个单峰分布之间的距离,而实际上目标和背景分布往往是呈多峰分布的,为此,本文提出了一种基于改进的巴氏距离的目标特征选择方法。对于每一个特征,用高斯混合模型对目标和背景建模,然后用改进的巴氏距离评估目标模型中每个高斯单元的区分能力,累加之和作为该特征的区分能力,并以此作为特征选择的依据。静态图像上的主观实验结果表明本文提出的度量方法能有效地度量两个多峰分布之间的距离,能够选择出具有高区分能力的特征,用这些高区分能力的特征做目标跟踪,显著提高了目标跟踪的稳定性。
3.针对传统的基于EM算法的高斯混合模型建模耗时太大问题,提出了一种在灰度和像素坐标的联合空间进行快速高斯混合建模的方法,并利用积分图像加快候选模型参数计算速度。同时,本文还提出了一种基于近似对称KL距离的度量方法来计算目标高斯混合模型与候选目标高斯混合模型之间的相似度。实验结果表明本文提出的建模方法可以大幅减少目标建模时间和候选模型参数估计时间,本文提出的度量方法具有较强的区分能力且稳定,显著地提升了目标跟踪性能。
4.现有空间直方图相似性度量方法要么不稳定,要么区分能力不够强,为此,本文提出了两种新的空间直方图相似性度量方法：一种是基于对称KL距离的度量方法,一种是基于改进JSD距离的度量方法。前者利用了对称KL距离具有较强的区分能力,后者是通过合理利用高斯的权重来加强JSD距离度量方法的区分能力。理论和实验证明了本文提出的两种度量方法既稳定,又具有较强的区分能力,优于现有的度量方法,目标跟踪性能得到了大幅度的提升。
With the development and popularization of computer science, electronic technology and artificial intelligence, object tracking technology has been widely applied to both civilian and military, such as visual surveillance, autonomous navigation, intellginet traffic monitor system, human-computer interaction system, video compression. In recent years, a large number of rearchers have studied the object tracking extensively and in-depth. Many effective video tracking algorithms are proposed for a variety of application environments. However, the object tracking system is quite complex.. Therefore, it is still one of the hot research fields of computer vision to develop a strong robustness and good applicability object tracking algorithm.
To deal with the difficult problem in tracking object, the research is focused on three major components of the object tracking system:feature selection, target presentation model, similarity measure and target localization algorithm in this dissertation. And several new effective methods have been proposed. The main contents and contribtuons of this dissertation are summarized as follows:
1. When the background and object have the similar color distribution, CAMSHIFT tracking algorithm can not successfully track the object. Thus, the robust distribution extraction algorithm based on adaptive Gaussian Mixture Model (GMM) is proposed. First, a fast and efficient adaptive GMM modeling method is used to model the target and background. The separability of each Gaussian component of object model is evaluated by the improved Bhattacharyya distance. The Gaussian components of object model that have a high separability are selected to generate the robust distribution. Experimental results show our algorithm can successfully extract the robust distribution and find the stable part of the object. When the robust distribution is applied to CAMSHIFT tracking algorithm, the accurate and robust tracking results are obtained and the tracking performance is improved.
2. The existing feature selection methods based on distance measure are mostly only suitable to measure the distance between the two unimodal distributions. However, in fact, the background and object distributions are often multimodal distribution. Therefore, we propose a new feature selection based on improved Bhattacharyya distance. For each feature, the object and background is modeled with a GMM. The improved Bhattacharyya distance is used to evaluate the discriminability of each Gaussian component of object model, which is added together to represent the separability of this feature. Feature selection is done according to the separability of each feature. The subjective experimental results on the static images show that our measure can effectively evaluate the distance between two multi-peak distributions, and select the most discriminative features to track the object, which helps to improve the tracking performance.
3. The traditional EM-based GMM modeling method is more time-consuming. A fast and effective GMM modeling method in a joint gray-spatial space is proposed. And the integral image is used to improve the computational efficiency of the candidate model parameters. Meanwhile, the approximate symmetric KL divergence between two GMMs is proposed to compute the similarity between the object GMM and the candidate GMM. The expremental results show the proposed modeling method can significantly reduce the target modeling time and the computational time of the parameters of the candidate model, and the proposed measure is robust and has a strong discriminability. Moreover, the tracking performance is significantly improved.
4. The existing spatiogram similarity measures are either not robust, or not discriminative enough. Two spatiogram similarity measures are proposed:one is based on the symmetric KL divergence, and another is based on improved Jensen-Shannon Divergence (JSD). The former takes advantage of the strong discriminability of the symmetric KL divergence. The latter makes full use of the weight of the Gaussian component to strengthen the discriminability of JSD. Theorectical and experimental results show that our proposed two measures are better than existing measures, and the tracking performace is greatly improved.
本文对目标跟踪系统中的目标特征选择、目标表示模型、相似性度量、目标定位算法这四个方面开展了深入研究,提出了一些新方法。论文的主要研究内容和成果概括如下：
1.当背景与目标的颜色分布比较相似时,CAMSHIFT目标跟踪算法就不能成功跟踪目标,为此,本文提出了一种基于自适应高斯混合模型的目标稳定分布提取算法。该算法首先用一种快速有效的自适应高斯混合模型建模方法对目标和背景建模,用改进的巴氏距离评估目标模型中所有高斯单元的区分能力,选择出具有较高区分能力的高斯单元,并由它们生成目标的稳定分布。实验结果表明该算法能够成功提取目标的稳定分布,并能找到目标中的稳定部分,将目标稳定分布应用到CAMSHIFT算法后能成功跟踪目标,从而提高了目标跟踪性能。
2.现有的基于距离度量的目标特征选择方法大多只适合度量两个单峰分布之间的距离,而实际上目标和背景分布往往是呈多峰分布的,为此,本文提出了一种基于改进的巴氏距离的目标特征选择方法。对于每一个特征,用高斯混合模型对目标和背景建模,然后用改进的巴氏距离评估目标模型中每个高斯单元的区分能力,累加之和作为该特征的区分能力,并以此作为特征选择的依据。静态图像上的主观实验结果表明本文提出的度量方法能有效地度量两个多峰分布之间的距离,能够选择出具有高区分能力的特征,用这些高区分能力的特征做目标跟踪,显著提高了目标跟踪的稳定性。
3.针对传统的基于EM算法的高斯混合模型建模耗时太大问题,提出了一种在灰度和像素坐标的联合空间进行快速高斯混合建模的方法,并利用积分图像加快候选模型参数计算速度。同时,本文还提出了一种基于近似对称KL距离的度量方法来计算目标高斯混合模型与候选目标高斯混合模型之间的相似度。实验结果表明本文提出的建模方法可以大幅减少目标建模时间和候选模型参数估计时间,本文提出的度量方法具有较强的区分能力且稳定,显著地提升了目标跟踪性能。
4.现有空间直方图相似性度量方法要么不稳定,要么区分能力不够强,为此,本文提出了两种新的空间直方图相似性度量方法：一种是基于对称KL距离的度量方法,一种是基于改进JSD距离的度量方法。前者利用了对称KL距离具有较强的区分能力,后者是通过合理利用高斯的权重来加强JSD距离度量方法的区分能力。理论和实验证明了本文提出的两种度量方法既稳定,又具有较强的区分能力,优于现有的度量方法,目标跟踪性能得到了大幅度的提升。
With the development and popularization of computer science, electronic technology and artificial intelligence, object tracking technology has been widely applied to both civilian and military, such as visual surveillance, autonomous navigation, intellginet traffic monitor system, human-computer interaction system, video compression. In recent years, a large number of rearchers have studied the object tracking extensively and in-depth. Many effective video tracking algorithms are proposed for a variety of application environments. However, the object tracking system is quite complex.. Therefore, it is still one of the hot research fields of computer vision to develop a strong robustness and good applicability object tracking algorithm.
To deal with the difficult problem in tracking object, the research is focused on three major components of the object tracking system:feature selection, target presentation model, similarity measure and target localization algorithm in this dissertation. And several new effective methods have been proposed. The main contents and contribtuons of this dissertation are summarized as follows:
1. When the background and object have the similar color distribution, CAMSHIFT tracking algorithm can not successfully track the object. Thus, the robust distribution extraction algorithm based on adaptive Gaussian Mixture Model (GMM) is proposed. First, a fast and efficient adaptive GMM modeling method is used to model the target and background. The separability of each Gaussian component of object model is evaluated by the improved Bhattacharyya distance. The Gaussian components of object model that have a high separability are selected to generate the robust distribution. Experimental results show our algorithm can successfully extract the robust distribution and find the stable part of the object. When the robust distribution is applied to CAMSHIFT tracking algorithm, the accurate and robust tracking results are obtained and the tracking performance is improved.
2. The existing feature selection methods based on distance measure are mostly only suitable to measure the distance between the two unimodal distributions. However, in fact, the background and object distributions are often multimodal distribution. Therefore, we propose a new feature selection based on improved Bhattacharyya distance. For each feature, the object and background is modeled with a GMM. The improved Bhattacharyya distance is used to evaluate the discriminability of each Gaussian component of object model, which is added together to represent the separability of this feature. Feature selection is done according to the separability of each feature. The subjective experimental results on the static images show that our measure can effectively evaluate the distance between two multi-peak distributions, and select the most discriminative features to track the object, which helps to improve the tracking performance.
3. The traditional EM-based GMM modeling method is more time-consuming. A fast and effective GMM modeling method in a joint gray-spatial space is proposed. And the integral image is used to improve the computational efficiency of the candidate model parameters. Meanwhile, the approximate symmetric KL divergence between two GMMs is proposed to compute the similarity between the object GMM and the candidate GMM. The expremental results show the proposed modeling method can significantly reduce the target modeling time and the computational time of the parameters of the candidate model, and the proposed measure is robust and has a strong discriminability. Moreover, the tracking performance is significantly improved.
4. The existing spatiogram similarity measures are either not robust, or not discriminative enough. Two spatiogram similarity measures are proposed:one is based on the symmetric KL divergence, and another is based on improved Jensen-Shannon Divergence (JSD). The former takes advantage of the strong discriminability of the symmetric KL divergence. The latter makes full use of the weight of the Gaussian component to strengthen the discriminability of JSD. Theorectical and experimental results show that our proposed two measures are better than existing measures, and the tracking performace is greatly improved.
引文
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