基于粒子滤波的行人跟踪算法研究
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摘要
目标跟踪在视频监控、智能人机交互、机器人视觉导航、智能交通、行为分析以及医疗诊断等方面有着广泛的应用。在目标跟踪的大多数场景中行人是跟踪的主要目标,行人跟踪在目标跟踪中有着重要的研究意义和应用价值。但是,由于人体运动的随意性,且经常会有光照、行人姿态变化、复杂背景以及遮挡等影响,使得复杂环境下行人跟踪仍然面临许多问题,而现有行人跟踪算法研究缺少鲁棒性好的观测模型,对行人特征进行描述来适应各种复杂环境,从而在现实中缺少适用性,也无法实现准确、鲁棒的跟踪；另一方面,目前多个行人的跟踪算法研究相对较少,已有的算法大多数只是在静态背景下进行行人跟踪,很少有实现移动背景的行人跟踪；另外,当前的行人跟踪研究多采用单个视频数据,存在跟踪视野范围小、信息量少且因视觉角度造成行人间的相互遮挡等缺点,因而在人数较多的场景下很难对其所有行人进行准确跟踪。
由于粒子滤波能够处理任意非线性、非高斯分布的系统,而该系统更能准确描述实际场景中的跟踪问题,因此本文基于粒子滤波理论,针对上述问题提出相应的行人跟踪算法,主要研究内容包含以下几个方面：
1)基于粒子采样优化的粒子滤波理论,通过多特征融合的方法来提高观测模型的鲁棒性,提出了模拟退火粒子群的粒子滤波多特征行人跟踪算法。本文从单人跟踪问题出发,针对粒子滤波算法中粒子多样性匮乏现象,根据粒子群与粒子滤波的相似性和模拟退火对粒子群全局极值条件的改进,采用模拟退火粒子群算法对粒子采样结果进行优化；接着围绕视频图像中单人跟踪问题,对状态空间模型进行改进,增强粒子对目标的跟踪能力；鉴于单个行人特征的观测模型在复杂环境和背景、噪声干扰等因素影响下所具有的目标识别能力有限,在设计观测模型时,结合三种相互间具有互补性的特征信息,并自适应的调整特征权重,从而提高算法在观测中对目标的鉴别。与单个特征的行人跟踪相比,算法在跟踪精度和稳定性都有所提高,且在目标平移、姿态变化、复杂背景以及部分遮挡等情况下仍然获得了较好的跟踪结果。
2)在静态背景和移动背景的行人跟踪中,引入特征包算法,将数量较大的特征集合转化为数量较小的特征字典和包来建立判决性模型,有效地解决多个行人特征提取复杂性的问题,提出了基于判决性模型的多人视频跟踪算法。在行人跟踪中,多个行人的跟踪与单个行人相比更为复杂,而采用多个特征进行多人跟踪会导致计算量较大,因而不适用于解决多人跟踪问题。鉴于此,本文引入计算简单、算法复杂度低的特征包算法,并结合超像素和局部二值模式(LBP)块特征的提取,共同建立判决性模型,来对视频序列中的行人进行判定。与常规跟踪算法不同,本文的多人跟踪算法在检测阶段提出了适用于静态背景和移动背景下的两种检测方法,提高了算法的适用性；同时,针对多人跟踪过程中经常会出现行人间的相互遮挡进行处理,以防止因目标遮挡引起的漂移和目标丢失现象。实验结果表明,提出的跟踪算法在处理如目标的平移、遮挡、行人间的干扰、光照和行走速度的变化以及相似物的干扰等复杂情况具有较好的稳定性和鲁棒性。
3)针对行人较密集场景中多个行人间的遮挡问题,结合视频图像的特征信息和激光点云的三维信息,提出了基于视频图像和激光点云的多人跟踪算法。在行人较多的场景,采用单一的视频数据因行人相互遮挡造成跟踪信息的缺失,因而很难对所有行人进行准确跟踪。因此,本文首先对激光点云数据进行解译、分类和检测,得出具有较好检测性能的分类参数,从而将行人和车辆数据分离出来；然后通过标定的方法实现视频图像和激光点云数据的融合,并利用两种数据间的互补性对融合后的兴趣区域进行分析；利用行人检测结果与粒子滤波跟踪算法的结合,对粒子状态与检测进行关联,并对行人目标的消失与出现情况进行处理,从而实现较密集场景下的行人跟踪。实验结果表明：两种数据的融合提高了行人检测的性能,并且在此基础上提出的多人跟踪算法能够处理行人间的相互遮挡、目标出现等情况,具有较好的跟踪效果。
Object tracking has a wide range of applications in video surveillance, intelligent human-computer interaction, visual navigation of robots, intelligent transportation, behavioral analysis and medical diagnosis, etc. Pedestrian is the main goal of tracking in most scenes of object tracking. Pedestrian tracking has important research significance and application value in object tracking. However, pedestrian tracking in complex environment is still facing many problems due to the arbitrariness of human motion, illumination and change of pedestrian postures, complex background and occlusion. Existing pedestrian tracking algorithms lack observation model of good robustness in describing the features to adapt to a variety of complex environments, thus they lack applicability in the real world and cannot achieve accurate robust tracking; on the other hand, the research in multiple pedestrians tracking algorithm is comparatively few. Most of the existing algorithms can achieve pedestrian tracking in the static background, few of them can achieve pedestrian tracking in the moving background; In addition, the current pedestrian tracking research often uses data of single video, which has shortcomings in small tracking field of view, less information, mutual occlusion due to the visual angle, etc, and thus has difficulties in achieving accurate tracking of all pedestrians in pedestrian intensive scenes.
Since particle filter is able to handle any non-linear and non-Gaussian distribution system which is more accurate in describing tracking problems in actual scenes, the dissertation puts forward pedestrian tracking algorithm based on particle filter theory in respond to the above issues. The main research content includes the following aspects:
1) Pedestrian tracking in video sequences based on multi-feature particle filter of simulated annealing and particle swarm is proposed based on the particle sampling optimized particle filter theory and the improved robustness of the observation model by multiple features fusion. Starting from the single pedestrian tracking problem, this dissertation uses simulated annealing and particle swarm algorithm to optimize particle sampling results based on the similarity between particle swarm and particle filter as well as the improved global extreme conditions of particle swarm by simulated annealing to deal with the particle diversity scarcity phenomenon in particle filter algorithm; then to solve the problem of single pedestrian tracking in video sequences, the state-space model is improved to enhance the particle's tracking capability of objects; in view of the limited ability of object recognition of the single pedestrian feature observation model under the influence of complex environment, background and noise interference factors, the dissertation combines with three types of feature information which have complementarities between each other and adjusts feature weights self-adaptively in designing the observation model to increase the object identification of algorithm in observations. Compared with pedestrian tracking using single feature, the proposed algorithm improves the accuracy and stability in tracking and can achieve better tracking results in the case of object translation, posture change, complex background, partial occlusion, etc.
2) Multi-pedestrian tracking algorithm in video sequences based on discriminative model is proposed by introducing bag of features algorithm into pedestrian tracking in both static and moving background which transforms a large number of feature sets into a smaller number of feature dictionaries and bags to create a discriminative model, and effectively solves complexity problem of multi-pedestrian feature extraction. Multi-pedestrian tracking is more complex than single pedestrian tracking, and using multiple features in multi-pedestrian tracking will cause a large amount of calculations, therefore it is not suitable to solve the problems of multi-pedestrian tracking. In view of this, the dissertation introduces a simple-calculating, low-complexity bag of features algorithm, and combines with superpixel and the extraction of LBP block features jointly to establish a discriminative model to determine the pedestrians in video sequences. Unlike conventional tracking algorithms, multi-pedestrian tracking algorithm in this dissertation proposes two detection methods which can be applied to both the static and moving background in the detection phase, thus improves the applicability of the algorithm; meanwhile, the problem of pedestrians' mutual occlusion in multi-pedestrian tracking process is processed in order to prevent the drift and lost phenomenon caused by the object block. Experimental results show that the proposed tracking algorithm has better stability and robustness in processing the objects' translation, occlusion, the interference among pedestrians, illumination, change in walking speed as well as analogue interference, etc.
3) For the problem of occlusion among multiple pedestrians in pedestrian intensive scenes, multi-pedestrian tracking algorithm based on video sequences and laser point clouds is proposed by combining feature information contained in video sequences and three-dimensional information of laser point clouds. It is difficult to accurately track all of the pedestrians by using data from single video in comparatively intensive scenes for the lack of tracking information caused by pedestrian occlusion. The algorithm first obtains classification parameters with better detection performance through interpreting, classifying and detecting laser point data to separate the data of pedestrians and vehicles, then achieves the fusion of video sequences and laser point clouds by the method of calibration, analyzes the region of interest after fusion by using the complementary between the two data; associates particle state with the detection by using a combination of pedestrian detection results with particle filter tracking algorithm; and processes the disappearance and appearance of pedestrian objects. As a result, pedestrian tracking in comparatively intensive scenes has been achieved. Experimental results show that the fusion of two data improves the performance of pedestrian detection and multi-pedestrian tracking algorithm based on that is able to handle pedestrians' mutual occlusion, object appearance, etc, thus has better tracking results.
由于粒子滤波能够处理任意非线性、非高斯分布的系统,而该系统更能准确描述实际场景中的跟踪问题,因此本文基于粒子滤波理论,针对上述问题提出相应的行人跟踪算法,主要研究内容包含以下几个方面：
1)基于粒子采样优化的粒子滤波理论,通过多特征融合的方法来提高观测模型的鲁棒性,提出了模拟退火粒子群的粒子滤波多特征行人跟踪算法。本文从单人跟踪问题出发,针对粒子滤波算法中粒子多样性匮乏现象,根据粒子群与粒子滤波的相似性和模拟退火对粒子群全局极值条件的改进,采用模拟退火粒子群算法对粒子采样结果进行优化；接着围绕视频图像中单人跟踪问题,对状态空间模型进行改进,增强粒子对目标的跟踪能力；鉴于单个行人特征的观测模型在复杂环境和背景、噪声干扰等因素影响下所具有的目标识别能力有限,在设计观测模型时,结合三种相互间具有互补性的特征信息,并自适应的调整特征权重,从而提高算法在观测中对目标的鉴别。与单个特征的行人跟踪相比,算法在跟踪精度和稳定性都有所提高,且在目标平移、姿态变化、复杂背景以及部分遮挡等情况下仍然获得了较好的跟踪结果。
2)在静态背景和移动背景的行人跟踪中,引入特征包算法,将数量较大的特征集合转化为数量较小的特征字典和包来建立判决性模型,有效地解决多个行人特征提取复杂性的问题,提出了基于判决性模型的多人视频跟踪算法。在行人跟踪中,多个行人的跟踪与单个行人相比更为复杂,而采用多个特征进行多人跟踪会导致计算量较大,因而不适用于解决多人跟踪问题。鉴于此,本文引入计算简单、算法复杂度低的特征包算法,并结合超像素和局部二值模式(LBP)块特征的提取,共同建立判决性模型,来对视频序列中的行人进行判定。与常规跟踪算法不同,本文的多人跟踪算法在检测阶段提出了适用于静态背景和移动背景下的两种检测方法,提高了算法的适用性；同时,针对多人跟踪过程中经常会出现行人间的相互遮挡进行处理,以防止因目标遮挡引起的漂移和目标丢失现象。实验结果表明,提出的跟踪算法在处理如目标的平移、遮挡、行人间的干扰、光照和行走速度的变化以及相似物的干扰等复杂情况具有较好的稳定性和鲁棒性。
3)针对行人较密集场景中多个行人间的遮挡问题,结合视频图像的特征信息和激光点云的三维信息,提出了基于视频图像和激光点云的多人跟踪算法。在行人较多的场景,采用单一的视频数据因行人相互遮挡造成跟踪信息的缺失,因而很难对所有行人进行准确跟踪。因此,本文首先对激光点云数据进行解译、分类和检测,得出具有较好检测性能的分类参数,从而将行人和车辆数据分离出来；然后通过标定的方法实现视频图像和激光点云数据的融合,并利用两种数据间的互补性对融合后的兴趣区域进行分析；利用行人检测结果与粒子滤波跟踪算法的结合,对粒子状态与检测进行关联,并对行人目标的消失与出现情况进行处理,从而实现较密集场景下的行人跟踪。实验结果表明：两种数据的融合提高了行人检测的性能,并且在此基础上提出的多人跟踪算法能够处理行人间的相互遮挡、目标出现等情况,具有较好的跟踪效果。
Object tracking has a wide range of applications in video surveillance, intelligent human-computer interaction, visual navigation of robots, intelligent transportation, behavioral analysis and medical diagnosis, etc. Pedestrian is the main goal of tracking in most scenes of object tracking. Pedestrian tracking has important research significance and application value in object tracking. However, pedestrian tracking in complex environment is still facing many problems due to the arbitrariness of human motion, illumination and change of pedestrian postures, complex background and occlusion. Existing pedestrian tracking algorithms lack observation model of good robustness in describing the features to adapt to a variety of complex environments, thus they lack applicability in the real world and cannot achieve accurate robust tracking; on the other hand, the research in multiple pedestrians tracking algorithm is comparatively few. Most of the existing algorithms can achieve pedestrian tracking in the static background, few of them can achieve pedestrian tracking in the moving background; In addition, the current pedestrian tracking research often uses data of single video, which has shortcomings in small tracking field of view, less information, mutual occlusion due to the visual angle, etc, and thus has difficulties in achieving accurate tracking of all pedestrians in pedestrian intensive scenes.
Since particle filter is able to handle any non-linear and non-Gaussian distribution system which is more accurate in describing tracking problems in actual scenes, the dissertation puts forward pedestrian tracking algorithm based on particle filter theory in respond to the above issues. The main research content includes the following aspects:
1) Pedestrian tracking in video sequences based on multi-feature particle filter of simulated annealing and particle swarm is proposed based on the particle sampling optimized particle filter theory and the improved robustness of the observation model by multiple features fusion. Starting from the single pedestrian tracking problem, this dissertation uses simulated annealing and particle swarm algorithm to optimize particle sampling results based on the similarity between particle swarm and particle filter as well as the improved global extreme conditions of particle swarm by simulated annealing to deal with the particle diversity scarcity phenomenon in particle filter algorithm; then to solve the problem of single pedestrian tracking in video sequences, the state-space model is improved to enhance the particle's tracking capability of objects; in view of the limited ability of object recognition of the single pedestrian feature observation model under the influence of complex environment, background and noise interference factors, the dissertation combines with three types of feature information which have complementarities between each other and adjusts feature weights self-adaptively in designing the observation model to increase the object identification of algorithm in observations. Compared with pedestrian tracking using single feature, the proposed algorithm improves the accuracy and stability in tracking and can achieve better tracking results in the case of object translation, posture change, complex background, partial occlusion, etc.
2) Multi-pedestrian tracking algorithm in video sequences based on discriminative model is proposed by introducing bag of features algorithm into pedestrian tracking in both static and moving background which transforms a large number of feature sets into a smaller number of feature dictionaries and bags to create a discriminative model, and effectively solves complexity problem of multi-pedestrian feature extraction. Multi-pedestrian tracking is more complex than single pedestrian tracking, and using multiple features in multi-pedestrian tracking will cause a large amount of calculations, therefore it is not suitable to solve the problems of multi-pedestrian tracking. In view of this, the dissertation introduces a simple-calculating, low-complexity bag of features algorithm, and combines with superpixel and the extraction of LBP block features jointly to establish a discriminative model to determine the pedestrians in video sequences. Unlike conventional tracking algorithms, multi-pedestrian tracking algorithm in this dissertation proposes two detection methods which can be applied to both the static and moving background in the detection phase, thus improves the applicability of the algorithm; meanwhile, the problem of pedestrians' mutual occlusion in multi-pedestrian tracking process is processed in order to prevent the drift and lost phenomenon caused by the object block. Experimental results show that the proposed tracking algorithm has better stability and robustness in processing the objects' translation, occlusion, the interference among pedestrians, illumination, change in walking speed as well as analogue interference, etc.
3) For the problem of occlusion among multiple pedestrians in pedestrian intensive scenes, multi-pedestrian tracking algorithm based on video sequences and laser point clouds is proposed by combining feature information contained in video sequences and three-dimensional information of laser point clouds. It is difficult to accurately track all of the pedestrians by using data from single video in comparatively intensive scenes for the lack of tracking information caused by pedestrian occlusion. The algorithm first obtains classification parameters with better detection performance through interpreting, classifying and detecting laser point data to separate the data of pedestrians and vehicles, then achieves the fusion of video sequences and laser point clouds by the method of calibration, analyzes the region of interest after fusion by using the complementary between the two data; associates particle state with the detection by using a combination of pedestrian detection results with particle filter tracking algorithm; and processes the disappearance and appearance of pedestrian objects. As a result, pedestrian tracking in comparatively intensive scenes has been achieved. Experimental results show that the fusion of two data improves the performance of pedestrian detection and multi-pedestrian tracking algorithm based on that is able to handle pedestrians' mutual occlusion, object appearance, etc, thus has better tracking results.
引文
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