基于改进Tracktor的行人多目标跟踪算法

在多目标视频跟踪中,针对受交互遮挡等影响导致检测偏差从而致使目标身份丢失的问题,提出一种基于改进Tracktor的行人多目标跟踪算法DUTracktor。在检测框回归中设计一个动态更新模块,利用孪生网络对建议框进一步检测定位;利用时序信息增强模块更新当前帧更适合的模板,建立全局上下文关系;并通过像素相关进行特征融合,从而增强目标边缘信息和尺度信息;利用相机运动补偿和融合相似矩阵构建二级关联跟踪机制,建立检测框和轨迹更强大的关联性,提高目标跟踪的鲁棒性。在公开的MOT16数据集上进行实验测试,并与当前主流算法相比,该算法跟踪精度表现较优,具有良好的鲁棒性,FPS稳定在24帧。

BEVTrack:基于难例挖掘训练的端到端三维多目标跟踪方法

多目标跟踪已经成为自动驾驶系统中的一个关键组成部分,其目的是在连续的视频流与点云流中识别、定位并标识所有感兴趣的目标。目前三维多目标跟踪方法多依赖人工多阶段调参以保证整体跟踪性能,难以对复杂遮挡或运动进行有效建模。而现有的三维端到端多目标跟踪方法,如MUTR等,精度普遍较低。其核心原因为三维空间中的特征聚合和感知相对于二维图像更具挑战性,简单的网络难以实现复杂的三维特征聚合,并大量的噪声信息与难例信息干扰严重,影响模型的特征提取能力。针对以上问题,本文提出了一种基于难例挖掘训练的端到端多目标跟踪框架BEVTrack。针对三维特征关联问题,本文设计了基于鸟瞰图(BEV)位置编码的三维跟踪查询。通过基于BEV特征的三维跟踪查询,本文方法能够更好地将跟踪查询与实际三维特征进行有效关联,从而大幅度提升了跟踪精度。同时,模型依靠BEV数据进行特征关联,仅需轻量化的网络便可以实现快速有效的跟踪。针对数据噪声问题,本文提出了面向多目标跟踪的难例挖掘训练,通过针对检测难例与跟踪难例分别处理,训练模型去除检测错误噪声与跟踪匹配的能力,从而提升在真实场景下模型处理噪声信息与难例干扰的能力。在实验结果方面,基于Nuscenes数据集,我们进行了大量的对比实验与模型消融实验,实验结果证明本文的方法在该数据集上取得了领先的性能。

基于改进CStrack关联策略的多目标跟踪算法

针对复杂场景下目标外观变化明显、运动不规律易导致轨迹中断和身份切换频繁等问题,从重识别(Re-Identification,Re-ID)特征、数据关联和插值等方面对跟踪器进行改进,提出基于改进CStrack关联策略的多目标跟踪算法。使用外观特征更新模块,减小因视角改变、目标移动导致特征剧烈变化而产生的影响,增强特征间的关联。提出二次关联方法,根据高低置信度检测结果的特点,使用不同的度量方式进行二次关联:第一次关联使用IoU距离融合外观特征作为关联的代价矩阵,第二次使用扩展IoU关联,缓解运动估计偏差、外观不可区分导致度量失效的问题;采用高斯回归算法,考虑运动信息,通过插值补偿漏检。在MOT17、MOT20数据集上进行测试,跟踪精度分别达到73.9%、64.2%。实验结果表明,该方法在跟踪精度上有明显优势,能够较好地适应复杂场景。

基于Bytetrack的多目标跟踪算法在斑马鱼毒性行为识别中的应用

利用计算机视觉技术识别斑马鱼(Danio rerio)在不同污染物暴露下的行为变化是水质毒性评价的常用方法之一,但传统方法存在效率低、面对遮挡和复杂环境时性能差等缺陷。针对这些问题,本研究使用基于Bytetrack的多目标跟踪算法追踪斑马鱼在4种污染物(Zn、Pb、Cr和苯酚)暴露2 h后的行为变化,对斑马鱼在4种浓度梯度中的平均速度、最大速度、最低速度、平均碰撞次数和行为轨迹等指标进行分析。结果显示,算法的追踪精度、漏检率和检测时间(每300帧)分别能达到90.26%、16.33%和0.19 min,检测时间和精度相比于传统目标检测方法有较大提升。同时,根据污染物不同,该方法能准确识别特定污染物环境中斑马鱼相应的运动状态及轨迹变化,可实现精确识别和实时响应,在鱼类毒性行为识别领域具有重要参考意义。

基于改进YOLOv7-ByteTrack的干制哈密大枣缺陷检测与计数系统

针对目前无法同时对随机多列排布干制哈密大枣进行快速缺陷检测和统计计数问题,该研究设计了一款干制哈密大枣在线检测与计数系统。以干制哈密大枣为研究对象,利用工业相机拍摄传送带上随机排列的多类别缺陷干制哈密大枣视频为数据源,采用改进的YOLOv7模型进行干制哈密大枣多类别缺陷检测并将检测结果作为后续多目标跟踪算法的输入;考虑到传送带上干制哈密大枣的外观相似性高以及排列密集等特点,该研究结合ByteTrack多目标跟踪算法的思想,设计了一种多类别干制哈密大枣的画线计数方法,实现了随机排布多类别干制哈密大枣的缺陷检测、准确定位及计数。试验结果表明:1)改进的YOLOv7模型浮点计算量为64.6 G,在干制哈密大枣目标检测数据的测试集上的平均检测精度、召回率、F_(1)平衡分数分别达到了98.03%、93.43%和95.00%,相比YOLOv7模型分别提高了4.40、6.88和7.00个百分点,浮点计算量下降了38.6%;2)基于改进YOLOv7为目标检测器开发的ByteTrack算法计数模型对干制哈密大枣计数的准确率为90.12%。该研究可为干制哈密大枣检测计数和分选分级提供技术支持。

改进YOLOv7+Bytetrack的小目标检测与追踪

近年来,目标检测技术已经相当成熟,但小目标检测一直是目标检测领域的一大挑战。为了解决这一问题,设计一种名为MFF-YOLOv7的小目标检测算法,该算法旨在提高小目标检测的准确率。设计级联双向特征金字塔KBiFPN,以及联合提出的多级感受野特征聚合模块MFA,来聚合浅层特征并增强特征的信息表达能力。为了解决小目标漏检问题,设计了新的解耦头和新的注意力机制。新的解耦头对小目标的检测能力更强,新的注意力机制可以重点关注感兴趣的小目标区域。引入了一种新的损失函数ECIOU,旨在加快模型的收敛速度。为了验证模型的性能,分别在三个小目标数据集上进行了实验。实验结果表明,MFF-YOLOv7算法提高了检测精度。同时,使用多目标追踪Bytetrack算法在MOT17和VisDrone2019-MOT两个多目标追踪数据集上对新模型进行了验证,进一步证明了其有效性。此外,MFF-YOLOv7算法在动态视频追踪中表现出了良好的性能。

基于状态向量增强ByteTrack的新生羔羊活动量自动计算方法

为评价母羊繁殖性能并及时发现分娩栏中的弱活力羔羊,该研究提出一种基于状态向量增强ByteTrack的新生羔羊活动量自动计算方法。针对传统ByteTrack算法在跟踪目标被遮挡时易发生身份切换的问题,引入置信度信息增强的状态向量,提高跟踪算法区分遮挡与被遮挡羔羊的能力。针对跟踪目标丢失导致轨迹预测不准确的问题,构建目标丢失期间的虚拟轨迹并重更新轨迹状态向量,以纠正轨迹误差。在获取各羔羊活动轨迹后,计算各羔羊帧间移动距离统计羔羊活动量。在江苏海门山羊研发中心采集的新生羔羊活动视频数据集上,测试状态向量增强的ByteTrack多目标跟踪算法性能。测试结果表明,研究提出的多目标跟踪方法在高阶跟踪精度、多目标跟踪精度、多目标跟踪准确度、IDF1得分上分别达到80.8%、86.1%、84.5%和92.2%,相较于现有算法的最高精度,分别提高2.7、0.2、2.3和3.9个百分点。该研究所提方法能够实现同窝多只新生羔羊的稳定跟踪,为新生羔羊活动量的自动计算、母羊繁殖性能的自动评估提供技术支撑。

Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

Label-free in-vivo classi-cation and tracking of red blood cells and platelets using Dynamic-YOLOv4 network

In-vivo flow cytometry is a noninvasive real-time diagnostic technique that facilitates continuous monitoring of cells without perturbing their natural biological environment,which renders it a valuable tool for both scienti¯c research and clinical applications.However,the conventional approach for improving classi¯cation accuracy often involves labeling cells with fluorescence,which can lead to potential phototoxicity.This study proposes a label-free in-vivo flow cytometry technique,called dynamic YOLOv4(D-YOLOv4),which improves classi¯cation accuracy by integrating absorption intensity°uctuation modulation(AIFM)into YOLOv4 to demodulate the temporal features of moving red blood cells(RBCs)and platelets.Using zebra¯sh as an experimental model,the D-YOLOv4 method achieved average precisions(APs)of 0.90 for RBCs and 0.64 for thrombocytes(similar to platelets in mammals),resulting in an overall AP of 0.77.These scores notably surpass those attained by alternative network models,thereby demonstrating that the combination of physical models with neural networks provides an innovative approach toward developing label-free in-vivoflow cytometry,which holds promise for diverse in-vivo cell classi¯cation applications.

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

Experimental and numerical settlement analysis of railway track over geogrid reinforced ballast

Purpose-The main objective of the present research is to investigate the benefits of using geogrid reinforcement in minimizing the rate of deterioration of ballasted rail track geometry resting on soft clay and to explore the effect of load amplitude,load frequency,presence of geogrid layer in ballast layer and ballast layer thickness on the behavior of track system.These variables are studied both experimentally and numerically.This paper examines the effect of geogrid reinforced ballast laying on a layer of clayey soil as a subgrade layer,where a half full scale railway tests are conducted as well as a theoretical analysis is performed.Design/methodologylapproach-The experimental tests work consists of laboratory model tests to investigate the reduction in the compressibility and stress distribution induced in soft clay under a ballast railway reinforced by geogrid reinforcement subjected to dynamic load.Experimental model based on an approximate half scale for general rail track engineering practice is adopted in this study which is used in Iraqi railways.The investigated parameters are load amplitude,load frequency and presence of geogrid reinforcement layer.A half fuli-scale railway was constructed for carrying out the tests,which consists of two rails 800 mm in iength with three w00den sleepers(900 mm×90 mm×90 mm).The ballast was overlying 500 mm thick clay layer.The tests were carried out with and without geogrid reinforcement,the tests were carried out in a well tied steel box of 1.5 m length X 1 m width X 1 m height.A series of laboratory tests were conducted to investigate the response of the ballast and the clay layers where the ballast was reinforced by a geogrid.Settlement in ballast and clay,was measured in reinforced and unreinforced ballast cases.In addition to the laboratory tests,the application of numerical analysis was made by using the finite element program PLAXIS 3D 2013.Findings-It was concluded that the settlement increased with increasing the simulated train load amplitude,there is a sharp increase in settlement up to the cycle 500 and after that,there is a gradual increase to level out between,2,500 and 4,500 cycles depending on the load frequency.There is a little increase in the induced settlement when the load amplitude increased from 0.5 to i ton,but it is higher when the load amplitude increased to 2 ton,the increase in settlement depends on the geogrid existence and the other studied parameters.Both experimental and numerical results showed the same behavior.The effect of load frequency on the settlement ratio is almost constant after 500 cycles.In general,for reinforced cases,the effect of load frequency on the settlement ratio is very small ranging between 0.5 and 2%compared with the unreinforcedcase.Originalitylvalue-Increasing the ballast layer thickness from 20 cm to 30 cm leads to decrease the settlement by about 50%.This ascertains the efficiency of ballast in spreading the waves induced by the track.

基于自编码器结构与改进Bytetrack的低光照行人检测及跟踪算法

针对夜间低光照场景下目标特征提取困难和跟踪不稳定的问题,提出了基于自编码器结构及改进Bytetrack的多目标行人检测及跟踪算法。在检测阶段,基于YOLOX(you only look once X)搭建多任务自编码变换模型框架,以一种自监督的方式考虑物理噪声模型和图像信号处理(image signal processing,ISP)的过程,通过对真实光照退化变换过程进行编码与解码学习内在视觉结构,并基于这种表示通过解码边界框坐标与类实现目标检测任务。为了抑制背景噪声的干扰,在目标解码器颈部网络引入自适应特征融合模块ASFF。跟踪阶段,基于Bytetrack算法进行改进,将基于Tranformer重识别网络提取到的外观嵌入信息与NSA卡尔曼滤波获得的运动信息通过自适应加权的方法完成数据关联,并通过Byte两次匹配的算法完成夜间行人的跟踪。在自建夜间行人检测数据集上测试检测模型的泛化能力,mAP@0.5达到了94.9%,结果表明本文的退化变换过程符合现实条件,具有良好的泛化能力。最后通过自建夜间行人跟踪数据集验证多目标跟踪性能,实验结果表明,本文提出的夜间低光照行人多目标跟踪算法MOTA(multiple object tracking accuracy)为89.55%,IDF1(identity F1 score)为88.34%,IDs(ID switches)为15。与基准方法Bytetrack相比,MOTA提高了10.72%,IDF1提高了6.19%,IDs减少了50%。结果表明,本文提出的基于自编码结构及改进Bytetrack的多目标跟踪算法可以有效解决在夜间低光照场景下行人跟踪困难的问题。

基于SBAS-InSAR与Offset-Tracking的色东普流域灾前形变探究

利用SAR偏移量追踪(offset-tracking)技术获取冰川形变作为SBAS-InSAR的补充,采用2种技术计算2018-01~10色东普流域灾前形变,联合分析灾前形变特征及影响因素。结果表明,色东普流域冰川与沟道在2018-10-17冰崩灾害发生前已出现形变;冰川主要形变区形变趋势表现为加速-平缓-加速,7~9月形变量达到-7.69 m;沟道内堆积物长期呈下滑趋势,7月后与冰川均加速形变;气温升高是冰崩碎屑流灾害发生的主导因素。联合SBAS-InSAR与offset-tracking技术能够满足不同形变量级的监测需求,可用于冰崩灾害的早期识别与形变反演,为青藏高原地区冰崩灾害防治提供参考。

Numerical analysis of high‑speed railway slab tracks using calibrated and validated 3D time‑domain modelling

Concrete slabs are widely used in modern railways to increase the inherent resilient quality of the tracks,provide safe and smooth rides,and reduce the maintenance frequency.In this paper,the elastic performance of a novel slab trackform for high-speed railways is investigated using three-dimensional finite element modelling in Abaqus.It is then compared to the performance of a ballasted track.First,slab and ballasted track models are developed to replicate the full-scale testing of track sections.Once the models are calibrated with the experimental results,the novel slab model is developed and compared against the calibrated slab track results.The slab and ballasted track models are then extended to create linear dynamic models,considering the track geodynamics,and simulating train passages at various speeds,for which the Ledsgard documented case was used to validate the models.Trains travelling at low and high speeds are analysed to investigate the track deflections and the wave propagation in the soil,considering the issues associated with critical speeds.Various train loading methods are discussed,and the most practical approach is retained and described.Moreover,correlations are made between the geotechnical parameters of modern high-speed rail and conventional standards.It is found that considering the same ground condition,the slab track deflections are considerably smaller than those of the ballasted track at high speeds,while they show similar behaviour at low speeds.

Determination of liquid viscosity based on dual-frequency-bandparticle tracking

An optical-tweezers-based dual-frequency-band particle tracking system was designed and fabricated for liquid viscositydetection. On the basis of the liquid viscosity dependent model of the particle’s restricted Brownian motion with theFax´en correction taken into account, the liquid viscosity and optical trap stiffness were determined by fitting the theoreticalprediction with the measured power spectral densities of the particle’s displacement and velocity that were derived from thedual-frequency-band particle tracking data. When the SiO2 beads were employed as probe particles in the measurements ofdifferent kinds of liquids, the measurement results exhibit a good agreement with the reported results, as well as a detectionuncertainty better than 4.6%. This kind of noninvasive economical technique can be applied in diverse environments forboth in situ and ex situ viscosity detection of liquids.

基于改进Bytetrack的群体机器人跟踪算法

针对群体机器人在实际跟踪场景中常常受到遮挡、目标密集、尺度变换等因素的影响导致漏检、轨迹中断和ID频繁跳变等问题,基于Bytetrack跟踪算法,改进了卡尔曼滤波的状态变量,提出了噪声尺度自适应卡尔曼算法(NASA-Kalman),并在卡尔曼滤波中引入加速度参数(AMA)提高跟踪的准确性。实验表明,在MOTA、MOTP方面相较于原算法均有所提高。为了进一步验证跟踪算法的有效性,在MOT20数据集上对算法进行了评估,在MOTA、MOTP方面分别提高了0.65%和1.26%。

FPGA and computer-vision-based atom tracking technology for scanning probe microscopy

Atom tracking technology enhanced with innovative algorithms has been implemented in this study,utilizing a comprehensive suite of controllers and software independently developed domestically.Leveraging an on-board field-programmable gate array(FPGA)with a core frequency of 100 MHz,our system facilitates reading and writing operations across 16 channels,performing discrete incremental proportional-integral-derivative(PID)calculations within 3.4 microseconds.Building upon this foundation,gradient and extremum algorithms are further integrated,incorporating circular and spiral scanning modes with a horizontal movement accuracy of 0.38 pm.This integration enhances the real-time performance and significantly increases the accuracy of atom tracking.Atom tracking achieves an equivalent precision of at least 142 pm on a highly oriented pyrolytic graphite(HOPG)surface under room temperature atmospheric conditions.Through applying computer vision and image processing algorithms,atom tracking can be used when scanning a large area.The techniques primarily consist of two algorithms:the region of interest(ROI)-based feature matching algorithm,which achieves 97.92%accuracy,and the feature description-based matching algorithm,with an impressive 99.99%accuracy.Both implementation approaches have been tested for scanner drift measurements,and these technologies are scalable and applicable in various domains of scanning probe microscopy with broad application prospects in the field of nanoengineering.

Beam Training and Tracking in mmWave Communication:A Survey

Communicating on millimeter wave(mmWave)bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission.However,mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles.To overcome these challenges,highly directional beams are exploited to achieve robust links by hybrid beamforming.Accurately aligning the transmitter and receiver beams,i.e.beam training,is vitally important to high data rate transmission.However,it may cause huge overhead which has negative effects on initial access,handover,and tracking.Besides,the mobility patterns of users are complicated and dynamic,which may cause tracking error and large tracking latency.An efficient beam tracking method has a positive effect on sustaining robust links.This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation(6G)mobile network.Especially,some open research problems are proposed to realize fast,accurate,and robust beam training and tracking.We hope that this survey provides guidelines for the researchers in the area of mmWave communications.

Automatic velocity picking based on optimal key points tracking algorithm

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.