基于深度学习的视频跟踪研究进展综述

近年来深度学习迅猛发展，颠覆了语音识别、图像分类、文本理解等领域的算法设计思路。深度学习因其具备强大的特征提取能力，在图像识别领域的成绩尤为突出。然而深度学习与视频监控领域的结合并不多，由于深度模型具有多层网络结构，算法复杂度大，训练和更新模型时比较耗时，很难满足实时性要求。回顾了深度学习的发展史，介绍了最近10年来国内外深度学习主要模型，论述了基于深度学习的目标跟踪算法，指出了各算法的优缺点，最后对当前该领域存在的问题和发展前景进行了总结和展望。     

Passive target detection and tracking from electromagnetic field measurements

This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to detect and track a possible target by resorting to a particle Bernoulli filter (PBF). To comply with real‐time operation, the PBF works along with an artificial neural network (ANN) model of the environment trained offline via finite elements (FEs). The performance of the proposed algorithm is assessed via simulation experiments.     

基于服务器端会话追踪的Web查询服务的研究

在探讨了Web查询服务的典型模型后,论文阐述了服务器端会话追踪机制的实现方式,设计了基于服务器端会话追踪的Web查询服务的典型解决方案。该设计方案可以较为安全地保存和维护客户的上下文信息,可靠地跟踪用户查询所处的状态,有效地实现有状态的Web查询服务。     

以一种新的距离度量作为反馈的相关跟踪方法

基于相关的模板匹配方法在图像跟踪中得到了广泛的应用,但是这种方法的主要缺点是对图像噪声非常敏感,而且由这种方法得出的相关曲面比较平坦。为了有效抑制噪声和局部遮挡对图像的影响,提出用鲁棒统计中的一个值代替相关值作为距离度量。它不是计算两幅图像的对应像素差值,而是计算两幅图像中差别不大的对应像素对的个数。实验证明这样得到的曲面相对　　收稿日期:2003 08 31;　修订日期:2003 10 28作者简介:张桂林(1944 ),男,天津人,教授,博士生导师,主要研究方向为自动识别、人体生物统计特征识别、图像处理算法与系统性能评估等。　　　　　　　要尖锐得多。当上述的距离度量应用于跟踪过程中时,模板的合理更新对跟踪非常重要。基于上述的距离度量方法,提出了一种新的模板更新策略。利用两幅图像中近似像素在整修匹配模板像素中所占的比例大小决定加权系数。这种加权策略可以自适应地根据图像序列的变化选择更新相关模板,同时在一定程度上减小模板漂移的危险。     

用DSP实现多目标实时跟踪

介绍了在 TMS3 2 0 C62 0 3上实现 4个目标的快速跟踪技术。为了提高实时性和精确度 ,采用线性汇编优化实现。1个 3 2 0× 160主窗口的 DSP平均处理时间为 2 m s,3个 60× 60副窗口的 DSP平均处理时间各为 0 .4ms,实现了复杂背景下低对比度目标的快速跟踪。实验结果证明跟踪效果良好 ,解决了以前窗口开不大、跟踪速度慢的问题     

目标被动式跟踪评述

首先介绍了目标被动跟踪的发展状况，然后按照不同的量测源分别对现有的被动跟踪方法进行讨论，并给出了各种方法的适用范围和各自的优缺点。最后试探性地给出目标被动跟踪的发展趋势，及其需要解决的重点、难点问题。     

一个新颖的轮廓线跟踪算法

提出了一个新颖的基于隐马尔科夫模型与光流的轮廓线跟踪算法。曲线描绘由B样条形状空间向量来表达,能够捕捉全局和局部变形。提出应用沿曲线的光流计算来预测曲线在下一帧的位置,在预测曲线的基础上,提出应用隐马尔科夫模型来准确定位曲线的位置。隐马尔科夫模型提供了一种有效的概率手段来融合多种量测特征比如边缘,曲线平滑性,区域灰度或颜色统计信息等等,能够更准确的定位曲线位置。基于仿射形状空间的实验了表明本文所提出算法的有效性。     

子空间方法盲多用户检测技术分析与探索

着重分析了基于子空间跟踪的盲线性多用户检测算法 ,并对其内部算法模块进行了仿真比较 ,最后对算法性能改善方法提出了自己的新思路     

Neural networks for nonlinear state estimation

Estimating the state of a nonlinear stochastic system (observed through a nonlinear noisy measurement channel) has been the goal of considerable research to solve both filtering and control problems. In this paper, an original approach to the solution of the optimal state estimation problem by means of neural networks is proposed, which consists in constraining the state estimator to take on the structure of a multilayer feedforward network. Both non-recursive and recursive estimation schemes are considered, which enable one to reduce the original functional problem to a nonlinear programming one. As this reduction entails approximations for the optimal estimation strategy, quantitative results on the accuracy of such approximations are reported. Simulation results confirm the effectiveness of the proposed method.     

An EM‐based adaptive multiple target tracking filter

Tracking targets of interest is one of the major research areas in radar surveillance systems. We formulate the problem as incomplete data estimation and apply EM to the MAP estimate. The resulting filter has a recursive structure analogous to the Kalman filter. The advantage is that the measurement‐update deals with multiple measurements in parallel and the parameter‐update estimates the system parameters on the fly. Experiments tracking separate targets in parallel show that tracking maintenance ratio of the proposed system is better than that of NNF and RMS position error is smaller than that of PDAF. Also, the system parameters are correctly obtained even from incorrect initial values. Copyright © 2001 John Wiley & Sons, Ltd.