OPTICS聚类与目标区域概率模型的多运动目标跟踪

目的 针对多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题,本文提出了一种基于OPTICS聚类与目标区域概率模型的方法。方法 首先引入了Harris-Sift特征点检测,完成相邻帧特征点匹配,提高了特征点跟踪精度和鲁棒性;再根据各运动目标与背景运动向量不同这一点,引入了改进后的OPTICS加注算法,在构建的光流图上聚类,从而准确的分离出背景,得到各运动目标的估计区域;对每个运动目标建立一个独立的目标区域概率模型(OPM),随着检测帧数的迭代更新,以得到运动目标的准确区域。结果 多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题通过本文方法得到了很好地解决,Harris-Sift特征点提取、匹配时间仅为Sift特征的17%。在室外复杂环境下,本文方法的平均准确率比传统背景补偿方法高出14%,本文方法能从移动背景中准确分离出运动目标。结论 实验结果表明,该算法能满足实时要求,能够准确分离出运动目标区域和背景区域,且对相机运动、旋转,场景亮度变化等影响因素具有较强的鲁棒性。     

A Competitive Neural Network for Multiple Object Tracking in Video Sequence Analysis

Tracking of moving objects in real situation is a challenging research issue, due to dynamic changes in objects or background appearance, illumination, shape and occlusions. In this paper, we deal with these difficulties by incorporating an adaptive feature weighting mechanism to the proposed growing competitive neural network for multiple objects tracking. The neural network takes advantage of the most relevant object features (information provided by the proposed adaptive feature weighting mechanism) in order to estimate the trajectories of the moving objects. The feature selection mechanism is based on a genetic algorithm, and the tracking algorithm is based on a growing competitive neural network where each unit is associated to each object in the scene. The proposed methods (object tracking and feature selection mechanism) are applied to detect the trajectories of moving vehicles in roads. Experimental results show the performance of the proposed system compared to the standard Kalman filter.     

多分辨率LK光流联合SURF的跟踪方法

针对交通监控中运动目标形变、雾霾天气、高速、光照不均、部分遮挡等复杂情况导致Lucas-Kanade（LK）算法跟踪不稳定问题,提出基于多分辨率LK光流算法联合快速鲁棒性特征（SURF）的跟踪算法。所提算法构建图像多分辨率小波金字塔,解决传统LK算法中同一像素点帧间大尺度运动易丢失问题;同时联合SURF尺度不变特征变换算法,提取特征点进行光流跟踪,并制定自适应模板实时更新策略;在减少光流计算量的同时增强运动目标抗复杂环境的能力。实验结果表明,新方法中特征点匹配准确快速,自适应性强,在交通复杂化境中跟踪稳定。     

一种基于SIFT特征光流的运动目标跟踪算法

针对传统光流跟踪算法计算复杂度高、受噪声影响大的问题,提出了一种基于尺度不变特征变换(Scale Invariant Feature Transform,SIFT)和卡尔曼滤波器的特征点光流跟踪算法。首先,利用SIFT算法提取图像中的特征点;然后,根据最小绝对值误差准则对运动目标的特征点进行匹配,建立卡尔曼滤波器方程来计算特征点光流;最后,通过光流特征聚类实现运动目标的识别与跟踪。实验结果表明,算法对自然场景中的运动目标具有良好的跟踪特性,稳定性好,计算量小,易于实现。     

融合图像显著性与特征点匹配的形变目标跟踪

目的 针对目标在跟踪过程中出现剧烈形变,特别是剧烈尺度变化的而导致跟踪失败情况,提出融合图像显著性与特征点匹配的目标跟踪算法。方法 首先利用改进的BRISK（binary robust invariant scalable keypoints）特征点检测算法,对视频序列中的初始帧提取特征点,确定跟踪算法中的目标模板和目标模板特征点集合;接着对当前帧进行特征点检测,并与目标模板特征点集合利用FLANN（fast approximate nearest neighbor search library）方法进行匹配得到匹配特征点子集;然后融合匹配特征点和光流特征点确定可靠特征点集;再后基于可靠特征点集和目标模板特征点集计算单应性变换矩阵粗确定目标跟踪框,继而基于LC（local contrast）图像显著性精确定目标跟踪框;最后融合图像显著性和可靠特征点自适应确定目标跟踪框。当连续三帧目标发生剧烈形变时,更新目标模板和目标模板特征点集。结果 为了验证算法性能,在OTB2013数据集中挑选出具有形变特性的8个视频序列,共2214帧图像作为实验数据集。在重合度实验中,本文算法能够达到0.567 1的平均重合度,优于当前先进的跟踪算法;在重合度成功率实验中,本文算法也比当前先进的跟踪算法具有更好的跟踪效果。最后利用Vega Prime仿真了无人机快速抵近飞行下目标出现剧烈形变的航拍视频序列,序列中目标的最大形变量超过14,帧间最大形变量达到1.72,实验表明本文算法在该视频序列上具有更好的跟踪效果。本文算法具有较好的实时性,平均帧率48.6帧/s。结论 本文算法能够实时准确的跟踪剧烈形变的目标,特别是剧烈尺度变化的目标。     

三焦点张量重投影视频稳像算法

目的 目前,特征点轨迹稳像算法无法兼顾轨迹长度、鲁棒性及轨迹利用率,因此容易造成该类算法的视频稳像结果扭曲失真或者局部不稳。针对此问题,提出基于三焦点张量重投影的特征点轨迹稳像算法。方法 利用三焦点张量构建长虚拟轨迹,通过平滑虚拟轨迹定义稳定视图,然后利用三焦点张量将实特征点重投影到稳定视图,以此实现实特征点轨迹的平滑,最后利用网格变形生成稳定帧。结果 对大量不同类型的视频进行稳像效果测试,并且与典型的特征点轨迹稳像算法以及商业软件进行稳像效果对比,其中包括基于轨迹增长的稳像算法、基于对极几何点转移的稳像算法以及商业软件Warp Stabilizer。本文算法的轨迹长度要求低、轨迹利用率高以及鲁棒性好,对于92%剧烈抖动的视频,稳像效果优于基于轨迹增长的稳像算法;对于93%缺乏长轨迹的视频以及71.4%存在滚动快门失真的视频,稳像效果优于Warp Stabilizer;而与基于对极几何点转移的稳像算法相比,退化情况更少,可避免摄像机阶段性静止、摄像机纯旋转等情况带来的算法失效问题。结论 本文算法对摄像机运动模式和场景深度限制少,不仅适宜处理缺少视差、场景结构非平面、滚动快门失真等常见的视频稳像问题,而且在摄像机摇头、运动模糊、剧烈抖动等长轨迹缺乏的情况下,依然能取得较好的稳像效果,但该算法的时间性能还有所不足。     

Tracking multiple features using relaxation

A new algorithm is introduced for tracking multiple features in an image sequence. First, the proposed method iteratively reduces the disparity of each possible match by relaxation labeling. It is assumed that all trajectories are smooth and the smoothness is used as the measure for correspondence. Some cases of wrong correspondences can be recovered by a proposed scheme called constraint-aided exchange during the tracking process. Occluded or missing feature points can be detected and predicted in the proposed algorithm. Finally, the algorithm is applied to data obtained from real world scenes. The human motion analysis can be achieved by the tracking algorithm.     

基于大数据分析的移动对象轨迹预测方法

对移动对象的轨迹预测将在移动目标跟踪识别中具有较好的应用价值。移动对象轨迹预测的基础是移动目标运动参量的采集和估计,移动目标的运动参量信息特征规模较大,传统的单分量时间序列分析方法难以实现准确的参量估计和轨迹预测。提出一种基于大数据多传感信息融合跟踪的移动对象轨迹预测算法。首先进行移动目标对象进行轨迹跟踪的控制对象描述和约束参量分析,对轨迹预测的大规模运动参量信息进行信息融合和自正整定性控制,通过大数据分析方法实现对移动对象运动参量的准确估计和检测,由此指导移动对象轨迹的准确预测,提高预测精度。仿真结果表明,采用该算法进行移动对象的运动参量估计和轨迹预测的精度较高,自适应性能较强,稳健性较好,相关的指标性能优于传统方法。     

随机边缘特征匹配在运动车辆跟踪算法中的应用

目标跟踪算法中往往需要有效的特征对目标进行跟踪识别。本文提出了随机边缘特征的概念,对其物理意义及匹配算法进行了阐述,并将其应用到运动车辆跟踪算法中。实验结果表明,该算法能够对序列图像中的多个运动车辆进行跟踪识别,对车辆的尺度、视角、光线及运动状态的变化等不利因素有较好的适应性。     

基于关键特征点的改进TLD目标跟踪算法研究

TLD（Tracking-Learning-Detection）算法是一种新颖的单目标长时间视觉跟踪算法,在给定极少的先验知识的情况下,能够迅速地学习目标特征并进行有效的跟踪。TLD算法中跟踪器每次在跟踪目标上均匀地选取特征点进行跟踪,不能保证每个特征点都能够被可靠地跟踪。针对这个问题,提出一种基于关键特征点检测的改进TLD算法,保证所选特征点都能够被正确可靠地跟踪,防止跟踪结果发生漂移,提高了跟踪器的跟踪精度。另一方面,在TLD检测器中引入了基于轨迹连续性的在线位置预测,在保证正确跟踪的前提下,缩小了检测器的检测范围,提高了运算速度。实验结果表明,该算法有较高的跟踪精度和速度。     

基于运动目标三维轨迹重建的视频序列同步算法

提出一种利用运动目标三维轨迹重建的视频时域同步算法.待同步的视频序列由不同相机在同一场景中同时拍摄得到,对场景及相机运动不做限制性约束.假设每帧图像的相机投影矩阵已知,首先基于离散余弦变换基重建运动目标的三维轨迹.然后提出一种基于轨迹基系数矩阵的秩约束,用于衡量不同序列子段间的空间时间对准程度.最后构建代价矩阵,并利用基于图的方法实现视频间的非线性时域同步.我们不依赖已知的点对应关系,不同视频中的跟踪点甚至可以对应不同的三维点,只要它们之间满足以下假设:观测序列中跟踪点对应的三维点,其空间位置可以用参考序列中所有跟踪点对应的三维点集的子集的线性组合描述,且该线性关系维持不变.与多数现有方法要求特征点跟踪持续整个图像序列不同,本文方法可以利用长短不一的图像点轨迹.本文在仿真数据和真实数据集上验证了提出方法的鲁棒性和性能.     

融合CN跟踪算法改进的TLD实时目标跟踪算法

针对TLD算法跟踪框在目标非刚性形变、旋转、背景杂乱等情景中容易导致跟踪漂移的问题,提出了一种融合CN跟踪算法改进的TLD实时目标跟踪算法(TLD-CN)。首先对跟踪框内区域计算图像显著性得到BRISK算法采样特征点的阈值,获得合适的特征点以建立旋转和尺度归一化的描述子,再融合颜色特征和纹理特征对前后帧跟踪框内描述子进行最优相似性匹配,得到匹配的特征点集合,对集合内特征点进行判别式字典的稀疏编码后,分别与CN跟踪框和TLD跟踪框的中心像素点进行相似度的度量,得到输出框调整的权重系数。实验结果表明,TLD-CN跟踪算法通过特征点度量出2种算法融合的权重值调整输出框,在目标形变、旋转、背景杂乱、快速运动等复杂跟踪情景中,具有很高的精度和成功率。权重系数自适应更新也避免了模型过拟合,达到实时跟踪效果。     

一种运动目标多特征点的鲁棒跟踪方法研究

提出了一种基于特征光流分割和卡尔曼滤波估计的鲁棒性的运动目标跟踪方法。该方法具有很多特点：首先在特征光流的计算中采用由粗到细的层级匹配算法，因而能够计算大的运动速度和具有更好的匹配精度；其次采用了有效的遮挡判决算法，该算法综合利用了先验的信息，对噪声的干扰不敏感；最后建立了线性卡尔曼滤波模型，当特征点被遮挡或丢失时，能够预测它们的位置，这使得跟踪更具有主动性。实验表明，该方法具有高精度、快速跟踪和很好的鲁棒性。     

A novel framework for motion segmentation and tracking by clustering incomplete trajectories

In this paper, we present a framework for visual object tracking based on clustering trajectories of image key points extracted from an image sequence. The main contribution of our method is that the trajectories are automatically extracted from the image sequence and they are provided directly to a model-based clustering approach. In most other methodologies, the latter constitutes a difficult part since the resulting feature trajectories have a short duration, as the key points disappear and reappear due to occlusion, illumination, viewpoint changes and noise. We present here a sparse, translation invariant regression mixture model for clustering trajectories of variable length. The overall scheme is converted into a maximum a posteriori approach, where the Expectation–Maximization (EM) algorithm is used for estimating the model parameters. The proposed method detects the different objects in the input image sequence by assigning each trajectory to a cluster, and simultaneously provides their motion. Numerical results demonstrate the ability of the proposed method to offer more accurate and robust solutions in comparison with other tracking approaches, such as the mean shift tracker, the camshift tracker and the Kalman filter.     

Multilinear Factorizations for Multi-Camera Rigid Structure from Motion Problems

Camera networks have gained increased importance in recent years. Existing approaches mostly use point correspondences between different camera views to calibrate such systems. However, it is often difficult or even impossible to establish such correspondences. But even without feature point correspondences between different camera views, if the cameras are temporally synchronized then the data from the cameras are strongly linked together by the motion correspondence: all the cameras observe the same motion. The present article therefore develops the necessary theory to use this motion correspondence for general rigid as well as planar rigid motions. Given multiple static affine cameras which observe a rigidly moving object and track feature points located on this object, what can be said about the resulting point trajectories? Are there any useful algebraic constraints hidden in the data? Is a 3D reconstruction of the scene possible even if there are no point correspondences between the different cameras? And if so, how many points are sufficient? Is there an algorithm which warrants finding the correct solution to this highly non-convex problem? This article addresses these questions and thereby introduces the concept of low-dimensional motion subspaces. The constraints provided by these motion subspaces enable an algorithm which ensures finding the correct solution to this non-convex reconstruction problem. The algorithm is based on multilinear analysis, matrix and tensor factorizations. Our new approach can handle extreme configurations, e.g. a camera in a camera network tracking only one single point. Results on synthetic as well as on real data sequences act as a proof of concept for the presented insights.     

面向卫星在轨实时处理的遥感视频动目标提取算法

针对现有动目标检测算法应用于卫星视频存在较多伪运动误检且难以在轨实时运行,同时短程跟踪算法难以寻回丢失目标的问题,提出一种卫星在轨实时提取运动目标算法.面向运动区域设计图像分类算法以优化运动检测结果,准确筛选动目标;用短程跟踪代替逐帧检测,以降低整体算法复杂度,并设计多特征融合与时空约束的重识别机制关联短程轨迹,应对跟...     

基于检测与跟踪相互迭代的极暗弱目标搜索算法

针对极低信噪比（LSNR）情况下暗弱运动目标和背景噪声的强度难以区分的问题,提出了一种基于检测与跟踪相互迭代的极暗弱目标搜索算法,总体上采用将时域检测与空域跟踪的过程联合、迭代进行的新型策略。首先,在检测过程中计算检测窗口内信号片段与已经提取的背景估计特征的差别;然后,在跟踪过程中运用动态规划算法保留使得轨迹能量累积最大的轨迹;最后,自适应地调整下一检测过程中被保留轨迹的检测器阈值参数,使该轨迹内的像素能以更宽容的策略被保留到下一检测跟踪阶段。实验测试结果表明,所提算法可以在1%~2%的虚警率和约70%的检测率下探测到低至0 dB的暗弱运动目标。可见该算法可有效改善对LSNR暗弱目标的检测能力。     

Faster CNN-based vehicle detection and counting strategy for fixed camera scenes

Automatic detection and counting of vehicles in a video is a challenging task and has become a key application area of traffic monitoring and management. In this paper, an efficient real-time approach for the detection and counting of moving vehicles is presented based on YOLOv2 and features point motion analysis. The work is based on synchronous vehicle features detection and tracking to achieve accurate counting results. The proposed strategy works in two phases; the first one is vehicle detection and the second is the counting of moving vehicles. Different convolutional neural networks including pixel by pixel classification networks and regression networks are investigated to improve the detection and counting decisions. For initial object detection, we have utilized state-of-the-art faster deep learning object detection algorithm YOLOv2 before refining them using K-means clustering and KLT tracker. Then an efficient approach is introduced using temporal information of the detection and tracking feature points between the framesets to assign each vehicle label with their corresponding trajectories and truly counted it. Experimental results on twelve challenging videos have shown that the proposed scheme generally outperforms state-of-the-art strategies. Moreover, the proposed approach using YOLOv2 increases the average time performance for the twelve tested sequences by 93.4% and 98.9% from 1.24 frames per second achieved using Faster Region-based Convolutional Neural Network (F R-CNN ) and 0.19 frames per second achieved using the background subtraction based CNN approach (BS-CNN ), respectively to 18.7 frames per second.

     

交互标记跟踪的三维动态数据对齐

针对三维动态数据特征点匹配所导致的错误对齐问题,采用交互标记和运动跟踪来提高特征点匹配的可靠性和稳定性。首先,对三维动态数据特定帧交互标定特征点;然后,通过运动跟踪和最优预测窗口得到标定特征点在其他帧上的位置;最后,以跟踪匹配的特征点为约束条件来构造等距二分图,得到三维动态数据紧密对齐结果。实验结果表明,所提算法的对齐准确率高于已有算法。     

基于特征点跟踪的运动目标接触时间估计方法研究

解决估计运动目标和静止观测者之间的接触时间(time-to-contact)的问题.首先定义了广义接触时间的概念,并提出了基于特征点跟踪的估计匀速运动目标接触时间的理论依据和利用特征线段估计接触时间的解决思路.随后,提出了一个结合Kalman滤波器的估计匀速运动目标和静止观测者之间接触时间的特征点跟踪方案,并讨论了特征点的选择准则、运动分割的方法、以及所采用的特征点跟踪的方法.最后,针对标定TTC的运动目标序列图像进行接触时间的估计实验,实验的结果是令人满意的.