低视点下遮挡自适应感知的多目标跟踪算法

目的 针对低视点多目标跟踪场景的遮挡问题,提出一种能够遮挡自适应感知的多目标跟踪算法。方法 首先根据每帧图像的全局遮挡状态,提出了“自适应抗遮挡特征”,增强目标特征对遮挡的感知和调整能力。同时,采用“级联筛查机制”,减少由遮挡带来的目标特征剧烈变化而认定为“虚新入目标”的错误跟踪现象。最后,考虑到历史模板库中存在遮挡的模板对跟踪性能的影响,根据每一帧中目标的局部遮挡状态,提出自适应干扰模板更新机制,进一步提高对遮挡的应变和适应能力。结果 实验结果表明,本文算法在MOTA(multiple object tracking accuracy)、M OTP (multiple object tracking precision)、FN(false negatives)、Rcll (recall)、ML (mostly lost tracklets)等指标上明显优于STAM(spatial-temporal attention mechanism)、ATAF(aggregate tracklet appearance features)、STRN (spatial-temporal relat...     

针对机动目标的改进型交互多模型跟踪算法研究

为了提高对机动目标的跟踪精度,更准确地获得目标实时位置与速度信息,提出了一种改进型交互多模型跟踪算法.采用目标特征数据为初始数据提供限定域,然后在滤波器中加入调节参数,从而利用目标状态增益矩阵与协方差矩阵的迭代完成对跟踪精度的优化.实验仿真分析了机动目标的3种常见状态,并与传统交互多模型跟踪算法进行了对比.实验结果显示...     

各向异性带宽自适应水面运动目标跟踪算法

传统Mean-Shift跟踪算法缺少核函数带宽更新策略,故无法解决无人艇跟踪的水面运动目标轮廓变化各向异性问题,提出一种各向异性带宽自适应的Mean-Shift跟踪算法。先用黎曼积分将特征子模型概率密度的归一化常数C_h近似为积分形式,从而获得不同尺度参数h对应的C_h间关系式。然后用梯度上升法使目标模型和目标候选模型之间的相似度函数达到局部最大,由此估计目标在下一帧的带宽与位置。最后为防止带宽更新时结果过小或过大,引入两个正则化参数修正尺度参数。实验结果表明,所提算法对外形轮廓非同比变化的水面运动目标跟踪具有各向异性的带宽自适应调节能力,型心位置准确率较传统Mean-Shift和各向同性带宽自适应Mean-Shift提高了约77.2%和31.1%,运行速度可达20.7 fps,显示了其鲁棒性和实时性。     

一种改进的运动目标跟踪与轨迹记录算法

针对目前运动目标的跟踪与记录方法占用存储空间较大的缺点,提出了一种减少存储空间的记录算法,即先用三帧差分算法和Snake算法相结合检出运动物体的轮廓,再利用Hausdorff算法对提出的轮廓进行匹配,并将匹配后的轮廓和运动轨迹以文本文件存储,大大降低了运动目标轨迹记录存储容量.实际运用表明,改进后的记录存储空间相当于通常视频文件的万分之一.该算法适于长时间记录运动目标轨迹.     

An efficient visual tracking method for multiple moving targets

An efficient algorithm of the edge detection according to integrating the edge gradient with the average filter is proposed, which can significantly reduce sensitivity of the background subtraction method to noise and illumination. Taking into account the features of the target such as color, size, etc., a new modified Nearest Neighbor （NN） algorithm for data association using the target features is designed. A designed Interacting Multiple Model （IMM） filter is utilized to track the maneuvering target motion, i.e. the feature point （called the centroid of the target） motion of the target. The algorithms are validated via an example with natural video sequences. The results show the algorithms are performances and validity for visual tracking. In complex environment, the algorithm can still work well.     

A particle swarm optimisation algorithm with interactive swarms for tracking multiple targets

We propose a novel particle swarm optimisation algorithm that uses a set of interactive swarms to track multiple pedestrians in a crowd. The proposed method improves the standard particle swarm optimisation algorithm with a dynamic social interaction model that enhances the interaction among swarms. In addition, we integrate constraints provided by temporal continuity and strength of person detections in the framework. This allows particle swarm optimisation to be able to track multiple moving targets in a complex scene. Experimental results demonstrate that the proposed method robustly tracks multiple targets despite the complex interactions among targets that lead to several occlusions.     

The multi‐iterative closest point tracker: An online algorithm for tracking multiple interacting targets

We describe and evaluate a greedy detection‐based algorithm for tracking a variable number of dynamic targets online. The algorithm leverages the well‐known iterative closest point (ICP) algorithm for aligning target models with target detections. The approach differs from trackers that seek globally optimal solutions because it treats the problem as a set of individual tracking problems. The method works for multiple targets by sequentially matching models to detections, and then removing detections from further consideration once models have been matched to them. This allows targets to pass close to one another with reduced risks of tracking failure due to “hijacking,'' or track merging. There has been significant previous work in this area, but we believe our approach addresses a number of tracking problems simultaneously that have only been addressed separately before. The algorithm is evaluated using four to eight laser range finders in three settings: quantitatively for a basketball game with 10 people and a 25‐person social behavior experiment, and qualitatively for a full‐scale soccer game. We also provide qualitative results using video to track ants in a captive habitat. During all the experiments, agents enter and leave the scene, so the number of targets to track varies with time. With eight laser range finders running, the system can locate and track targets at sensor frame rate 37.5 Hz on commodity computing hardware. Our evaluation shows that the tracking system correctly detects each track over 98% of the time. © 2012 Wiley Periodicals, Inc.     

基于MMPHDF的多机动目标联合检测、跟踪与分类算法

针对场景中存在新目标出现、旧目标消失(即目标数目变化)和密集杂波的复杂情形,利用多模型概率假设密度滤波器(MMPHDF)在多机动目标联合检测与跟踪上的优势,加入类别辅助信息,提出了一种多机动目标联合检测、跟踪与分类算法.该算法的基本思想是在MMPHDF中用属性向量扩展单目标状态向量,用位置和属性的组合测量似然函数代替单目标位置及杂波位置测量似然函数,提高了不同类目标与杂波测量间的鉴别能力,从而改善了目标数目及状态的估计精度;在更新目标状态后,对目标属性信息进行更新,更为精确的目标数目及状态估计又保证了目标分类性能.本文给出了该算法的粒子实现方法.仿真结果验证了上述结论.     

A novel multiple maneuvering targets tracking algorithm with data association and track management

A novel multiple maneuvering targets tracking algorithm with data association and track management is presented in this paper. First, the variation of the generalized pseudo-Bayesian estimator of first order is designed. Then, the data association and track management via handling two matrices are given, which reflect the relationships between target trajectory and the output of the Gaussian mixture probability hypothesis density (PHD) filter for jump Markov system models (JMS-GM-PHD) filter. The tracking performance of the proposed algorithm is compared with two conventional algorithms. One is JMS-GM-PHD filter, the other is algorithm entitled hybrid algorithms for multi-target tracking using MHT and GM-CPHD which is denoted as hybrid method hereinafter. The results of Monte Carlo simulation show that the proposed filter has overall performance than the conventional.     

Sequential stratified sampling belief propagation for multiple targets tracking

1 Introduction Multiple targets tracking (MTT) remains a challenging problem in computer vision. It has been applied widely in the intelligent surveillance, visual human-computer interface, and smart conference room among many others. MTT problem has been…     

Closed-world tracking of multiple interacting targets for indoor-sports applications

In this paper we present an efficient algorithm for tracking multiple players during indoor sports matches. A sports match can be considered as a semi-controlled environment for which a set of closed-world assumptions regarding the visual as well as the dynamical properties of the players and the court can be derived. These assumptions are then used in the context of particle filtering to arrive at a computationally fast, closed-world, multi-player tracker. The proposed tracker is based on multiple, single-player trackers, which are combined using a closed-world assumption about the interactions among players. With regard to the visual properties, the robustness of the tracker is achieved by deriving a novel sports-domain-specific likelihood function and employing a novel background-elimination scheme. The restrictions on the player’s dynamics are enforced by employing a novel form of local smoothing. This smoothing renders the tracking more robust and reduces the computational complexity of the tracker. We evaluated the proposed closed-world, multi-player tracker on a challenging data set. In comparison with several similar trackers that did not utilize all of the closed-world assumptions, the proposed tracker produced better estimates of position and prediction as well as reducing the number of failures.     

Adaptive swarm optimization for locating and tracking multiple targets

Locating and tracking multiple targets in the dynamic and uncertain environment is a crucial and challenging problem in many practical applications. The main task of this paper is to investigate three fundamental problems, which are composed of the identification of irregular target, locating multiple targets and tracking multiple targets. Firstly, the proposed objective function successfully gets the target's shape to discern eccentric target in the specific environment. Secondly, for the sake of locating multiple targets, the adaptive PSO algorithm divides the swarm into many subgroups, and adaptively adjusts the number of particles in each subgroup by the competition and cooperation technology. Thirdly, in order to track multiple targets in the dynamic environment, the proposed swarm optimization has the characteristic of the adaptively covered radius of the subgroup according to the minimum distance among other subgroups. To show the efficiency and high performance of the proposed algorithms, several algorithms chiefly concentrate on locating and tracking three ants in the practical systems.     

弱点状多运动目标实时跟踪技术研究

根据确认的众多量测和众多目标跟踪窗之间的几何关系,引入确认矩阵并计算所有联合事件及其对应的参数,不论量测是否落入跟踪窗相交区域,根据JPDA算法计算每一个量测与其可能的各个源目标之间互联的概率。将互联的概率与Kalman滤波器相结合从而完成对每一个目标的预测和更新。理论及实验结果表明,该算法适用于序列图像密集杂波环境下的全程跟踪,并取得了一定的理论和仿真结果。     

强跟踪输入估计概率假设密度多机动目标跟踪算法

针对多机动目标跟踪中,目标数目未知及加速度不确定的问题,提出一种强跟踪输入估计（modifiedinputestimation,MIE）概率假设密度多机动目标跟踪算法．在详细分析算法的基础上,通过引入强跟踪多重渐消因子,以不同速率实时调节滤波器各个通道的预测协方差及相应的滤波器增益,从而实现MIE算法对加速度未知或发生人幅度突变的机动目标白适应跟踪能力;并将该算法与概率假设密度滤波算法有效结合,町以较好地跟踪未知数目的多机动目标．仿真结果表明,新算法比传统的多机动目标跟踪算法具有更岛的跟踪精度,且具有较好的实时性．     

基于分层光流的煤矿井下运动目标跟踪算法

针对煤矿井下视频序列高噪声、运动目标繁多、目标交错等特点,提出了一种基于分层光流法的矿井运动目标跟踪算法。首先对相邻两帧视频序列采用帧差法确定多个初始目标跟踪模版;然后采用SUSAN角点检测提取模版内的特征点;最后运用改进的分层光流法进行目标检测和跟踪,并在跟踪过程中对模版进行更新。该算法实现了煤矿井下多目标运动物体的稳定跟踪,并能解决目标部分遮挡问题。实验和仿真结果证实了该算法的有效性。     

基于箱粒子的多扩展目标PHD滤波

在高斯混合多扩展目标PHD 滤波的基础上, 结合最新兴起的箱粒子滤波, 提出一种基于区间分析的多扩展目标PHD 滤波算法. 采用大小可控的非零矩形区域来代替传统的多个点量测, 这样可降低权值计算中对量测分布的要求. 仿真对比实验表明, 采用区间分析方法在保证近似于传统滤波精度的同时可降低计算复杂度, 在目标数目估计及抗杂波干扰方面也具有较为突出的优势, 并且可解决在目标靠近时由于不能正确给出子划分而造成的漏检问题.

     

几种典型的微弱点状多运动目标跟踪算法对比研究

重点研究了序列图像情况下几种典型的微弱点状多运动目标实时跟踪算法,虽然它们都能够完成不同背景环境下目标的全程跟踪,但跟踪性能存在较大的差异,PDA算法具有较高的实时性,但容易出现目标的偏移和聚合现象;JPDA算法理论上解决了多目标数据关联问题,但跟踪过程存在较大误差且由于计算量大难以在工程中应用;基于最大熵高斯聚类算法对模糊隶属度进行了修正,数据关联性高且有效避免了目标的误跟和丢失现象。通过对几种典型算法的仿真分析,为多目标跟踪算法的优化提供可靠依据。     

三维高速机动目标跟踪交互式多模型算法

对三维机动目标,尤其是高速机动目标的跟踪一直是目标跟踪领域的重点和难点,通过二维或解耦模型扩展并不一定能满足精度要求.提出了一种基于常速模型、"当前"统计模型、带约束的三维常速率协同转弯模型的交互式多模型算法.通过对包括匀加速模型、Singer模型、"当前"统计模型在内的不同模型组合进行Monte-Carlo仿真比较表明本算法对三维高速机动目标跟踪是有效性,并具有很好的实用性.     

基于概率假设密度的近邻目标快速跟踪算法

针对杂波场景中多个目标相互近邻时,标准概率假设密度滤波器难以正确估计目标状态且计算复杂度高等问题,提出一种基于概率假设密度的近邻目标快速跟踪算法。所提算法首先采用自适应门技术从传感器观测集中划分出源于真实目标的观测集,随后利用真实目标观测集来更新预测强度,最后应用检测导向的近邻目标权重校正方法有选择地重分配各离散时刻后验强度中不精确的分量权重。实验结果表明,所提算法不仅具有高效率的目标跟踪,而且具有良好的鲁棒性。     

A model and tracking algorithm for a class of video targets

Algorithms are developed for a real-time, automatic system capable of tracking two-dimensional (2-D) targets in complex scenes. A mathematical model of 2-D image spatial and temporal evolution applicable to certain classes of targets and target perturbations is developed. It is shown that for small target perturbations the 2-D tracking problem may be approximated as a 1-D time-varying parameter estimation problem. A CCD-implementable solution is developed which is particularly useful when considering target rotation and dilation and target/background separation. Results of system simulation and suggestions for future research are presented.