Joint tracking and classification of extended targets with complex shapes

This paper addresses the problem of joint tracking and classification(JTC) of a single extended target with a complex shape. To describe this complex shape, the spatial extent state is first modeled by star-convex shape via a random hypersurface model(RHM), and then used as feature information for target classification. The target state is modeled by two vectors to alleviate the influence of the high-dimensional state space and the severely nonlinear observation model on target state estimation, while the Euclidean distance metric of the normalized Fourier descriptors is applied to obtain the analytical solution of the updated class probability. Consequently, the resulting method is called the "JTC-RHM method." Besides, the proposed JTC-RHM is integrated into a Bernoulli filter framework to solve the JTC of a single extended target in the presence of detection uncertainty and clutter, resulting in a JTC-RHM-Ber filter. Specifically, the recursive expressions of this filter are derived. Simulations indicate that:(1) the proposed JTC-RHM method can classify the targets with complex shapes and similar sizes more correctly, compared with the JTC method based on the random matrix model,(2) the proposed method performs better in target state estimation than the star-convex RHM based extended target tracking method,(3) the proposed JTC-RHM-Ber filter has a promising performance in state detection and estimation, and can achieve target classification correctly.

End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.

Modified joint probabilistic data association with classification-aided for multitarget tracking

Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.

An Automated Player Detection and Tracking in Basketball Game

Vision-based player recognition is critical in sports applications.Accuracy,efficiency,and Low memory utilization is alluring for ongoing errands,for example,astute communicates and occasion classification.We developed an algorithm that tracks the movements of different players from a video of a basketball game.With their position tracked,we then proceed to map the position of these players onto an image of a basketball court.The purpose of tracking player is to provide the maximum amount of information to basketball coaches and organizations,so that they can better design mechanisms of defence and attack.Overall,our model has a high degree of identification and tracking of the players in the court.We directed investigations on soccer,basketball,ice hockey and pedestrian datasets.The trial comes about an exhibit that our technique can precisely recognize players under testing conditions.Contrasted and CNNs that are adjusted from general question identification systems,for example,Faster-RCNN,our approach accomplishes cutting edge exactness on three sorts of recreations(basketball,soccer and ice hockey)with 1000×fewer parameters.The all-inclusive statement of our technique is additionally shown on a standard passer-by recognition dataset in which our strategy accomplishes aggressive execution contrasted and cutting-edge methods.

Passive target tracking with intermittent measurement based on random finite set

In the tracking problem for the maritime radiation source by a passive sensor,there are three main difficulties,i.e.,the poor observability of the radiation source,the detection uncertainty(false and missed detections)and the uncertainty of the target appearing/disappearing in the field of view.These difficulties can make the establishment or maintenance of the radiation source target track invalid.By incorporating the elevation information of the passive sensor into the automatic bearings-only tracking(BOT)and consolidating these uncertainties under the framework of random finite set(RFS),a novel approach for tracking maritime radiation source target with intermittent measurement was proposed.Under the RFS framework,the target state was represented as a set that can take on either an empty set or a singleton; meanwhile,the measurement uncertainty was modeled as a Bernoulli random finite set.Moreover,the elevation information of the sensor platform was introduced to ensure observability of passive measurements and obtain the unique target localization.Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source and demonstrate the superiority of the proposed approach in comparison with the traditional integrated probabilistic data association(IPDA)method.The tracking performance under different conditions,particularly involving different existence probabilities and different appearance durations of the target,indicates that the method to solve our problem is robust and effective.

Data association based on target signal classification information

In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.

Internal Defects Detection Method of the Railway Track Based on Generalization Features Cluster Under Ultrasonic Images

There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.

基于眼动信号的感兴趣检测方法研究

为了使机器智能达到人类的识别能力,需要提供标注的难例样本。常用的键盘、鼠标等标注方式效率较低,基于眼动信号的标注方式无需手动操作,但目前研究多采用依赖特征工程的浅层模型实现感兴趣检测以标注样本。针对浅层模型存在的问题,基于特征通道权重重分配多尺度残差网络模型对注视序列分类以实现感兴趣检测,并通过对比实验验证本文方法的有效性。实验结果表明:提出的多尺度残差网络模型分类精度达到96%,较现有基于浅层模型的方法和未改进的基于深层模型的方法,显著提升了感兴趣检测的精度和鲁棒性。

基于视觉的渔船监管关键技术研究与系统开发

当前小型渔船监管的信息化水平不高,依赖人工巡查监管存在漏检误检,易导致渔业生产安全隐患。针对此问题,首先,构建了小型渔船的进出港目标识别以及人员安全检查的系列深度学习模型;其次,开发了一套渔船进出港监管系统,实现了渔业生产管理的智能化;最后,将监管系统成功部署,促进了烟台长岛渔业生产监管的智能化。该系统实现了渔船及人员目标检测与轨迹追踪、船牌识别、救生衣穿着检测等功能,并与年审渔船信息以及违禁出海管理等对接,实现对渔船未年审出海、违章载客、未穿救生衣、违禁出海等违规活动的告警。研究工作提高了渔船进出港监管效率,降低了渔业生产的安全隐患。

基于改进YOLOv5s算法的钢轨扣件状态检测方法

[目的]为提高轨道巡检效率以及优化巡检策略,有效避免漏巡、漏检等问题的发生,提出了一种基于改进YOLOv5s算法的钢轨扣件状态检测方法。[方法]介绍了YOLOv5算法的网络结构。在YOLOv5s算法的骨干网络中融入C3-CBAM(卷积注意力)模块以获取更多细节特征,然后采用BiFPN(加权双向特征金字塔)网络进行多尺度特征融合,形成改进YOLOv5算法。针对弹条断裂、弹条缺失、弹条移位和螺栓缺失4种状态进行了试验验证。[结果及结论]采用改进后的YOLOv5s算法比原YOLOv5s算法在测试精度、召回率、平均精度上都有所提高,表明该方法对钢轨故障扣件分类检测具有很好的工程应用价值。

基于LGJMS-GMPHDF的多机动目标联合检测、跟踪与分类算法

线性高斯跳变马尔可夫系统模型下的高斯混合概率假设密度滤波器(LGJMS-GMPHDF)为杂波背景下多机动目标跟踪提供了一种有效方法。该文将类别辅助信息引入LGJMS-GMPHDF,提出了一种密集杂波背景下多机动目标联合检测、跟踪与分类算法。该算法在LGJMS-GMPHDF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,进而改善了目标数目及状态的估计精度;在更新目标状态的同时,对目标属性信息进行更新。该算法实现了时变数目的目标状态和类别估计。杂波背景下交叉和临近并行机动目标的跟踪实验验证了该文算法的联合检测、跟踪与分类性能。

联合目标跟踪与分类技术的进展及存在问题

联合目标跟踪与分类(JTC)技术是信息融合领域新兴的一个研究方向。其基本思想是:通过在目标跟踪器和目标分类器之间进行双向信息交互,来同时有效地提高目标的跟踪精度和分类性能。介绍了JTC技术的基本原理。将现有JTC技术划分为两大类——基于质点运动模型的JTC技术和基于刚体运动模型的JTC技术,并作了对比分析。综合论述了JTC技术的发展。指出JTC技术目前存在的主要问题以及未来的研究方向。

基于PHD的多扩展目标联合检测、跟踪与分类算法

针对杂波和噪声背景下空间距离较近的扩展目标数目和状态难以估计的问题,提出了基于扩展目标概率假设密度滤波器(ET-PHD)的多目标联合检测、跟踪与分类算法,并给出了该算法基于粒子滤波的实现方法.算法在滤波器中引入了属性量测信息,预测阶段的粒子按照其类别进行传播,更新阶段对所有粒子进行联合更新,更新结束后将粒子按照类别进行分类,各类别的粒子集表示了其相应类别目标的PHD分布.该算法具有模块化结构,计算复杂度为O(mn).数值仿真场景包含两类扩展目标并行运动和两类扩展目标交叉运动.结果表明,该算法可以同时估计扩展目标的类别、数目和状态,并且平均最优次模式分配距离相比传统算法的降低幅度超过50%.

基于有限集统计学理论的目标跟踪技术研究综述

有限集统计学理论为杂波背景下的目标跟踪问题提供了一种工程友好的理论工具.对近年来基于有限集统计学理论的目标跟踪技术研究现状进行了综述,包括最优多目标贝叶斯滤波器及其近似技术、参数未知与机动多目标跟踪技术、航迹生成方法、单目标联合检测与跟踪滤波器及基于有限集观测的单目标滤波器等,对相关应用亦有所介绍.最后在已有研究发展的基础上,着眼于提高目标跟踪精度和增强目标跟踪鲁棒性的发展需要,提出了基于有限集统计学理论的目标跟踪技术需重点解决和关注的若干问题,包括多目标跟踪性能评价、弱小目标跟踪、多机动目标跟踪、多传感器融合跟踪以及联合目标检测、跟踪与分类等方面.

互联网话题识别与跟踪系统设计及实现

针对互联网上论坛和新闻网站发布的海量自然语言文本,该文设计一个话题识别与跟踪系统,将海量的数据分类整理并聚合形成各个话题。该系统的核心采用SVM方法进行文本分类,基于知识库和网络流算法实现话题的聚合,测试结果表明,文章分类的正确率达到92%,聚类的正确率达到88%,具有较高的应用价值。

基于北斗短报文的震源船跟踪系统设计与实现

通过在震源船上部署3套GPS接收机,并对3个点的位置信息进行汇总和压缩,利用北斗短报文将压缩后的位置信息推送到陆海联测指挥部,实现对震源船航速、航向的实时监控.根据3个点位信息计算的实时震源船航向,有助于准确把握震源船在进行固定点悬停激发时的航向,克服了以往使用前后两个时刻推算的平均航向代替实时航向等问题.使用我国自主知识产权的北斗短报文进行通讯,大大提高了系统的安全性、可靠性和稳定性;同时陆海联测指挥部可以实时查看震源船是否按事先设定的测线、事先设定的速度和航向进行作业,有效保障了监控平台对震源船的监控,进一步提升安全生产的风险控制能力.

视频监控技术的发展与现状

视频监控技术为重要场所安全防护提供了可靠保障,随着视频监控系统应用领域的扩大,视频监控技术得到了迅速发展。首先对视频监控技术的发展历程分三个阶段进行了概括,其次对视频监控中的关键技术的现状和发展方向进行了分析,其中重点描述了运动检测中的主流方法———背景差方法,目标跟踪中用于解决遮挡问题的合并分裂方法和直接穿透方法,最后提出了视频监控技术在实际应用中所面临的问题。

城市轨道交通智能视频分析关键技术综述

轨道交通是改善城市公共交通状况的有效途径。随着城市轨道交通的快速建设,人们对城市轨道交通的安全问题越来越重视。智能视频分析技术通过对监控视频流的实时分析,对场景中的各种目标进行检测、分类、跟踪,并分析和判断目标的行为,从而能在异常情况发生时可以及时报警、主动防范,提高处理突发事件的效率。主要研究了智能视频分析技术在轨道交通智能视频监控系统中的应用背景及技术框架,总结了智能视频分析中的关键技术的不同实现方法及其常见的算法。

矿山岩石节理裂隙检测方法综述

岩石节理裂隙的精确检测是判定矿山开挖稳定性的关键,一般的检测步骤是从原始图像中提取出有效的裂隙信息,通过识别感兴趣区域来获取特征知识,从而完成对图像的理解。首先对矿山岩石节理裂隙的特征进行概括分析;然后对近30 a来岩石节理裂隙检测方法进行分类,系统地介绍了模式识别方法、数学形态学方法和分形学方法的理论依据、优缺点以及研究成果,并对这几种方法进行了分析比较;最后对岩石节理裂隙检测方法的现状及发展趋势进行了总结。

基于HSV特征变换与目标检测的变压器呼吸器缺陷智能识别方法

为了利用深度学习算法提高变压器运维的智能化水平,提出了一种基于色相饱和度值(hue-saturation-value,HSV)特征变换与目标检测的变压器呼吸器缺陷智能识别方法。该方法利用单发多盒探测器(single shot multibox detector,SSD)网络框架进行呼吸器目标提取,采用HSV颜色转换完成空间映射,通过设定HSV特征阈值进行呼吸器正常颜色和异常颜色的跟踪和提取,进而通过各颜色分量比例与分布情况进行呼吸器状态的智能判断。研究结果表明:所提识别方法能够利用图像特征对变压器呼吸器进行准确定位与状态识别。论文研究可为电力设备锈蚀识别等其他类似场景提供参考。