背景对齐差分的机场跑道异物分块检测与跟踪算法

针对机场跑道异物的无人自主检测与识别过程中存在的检测结果受环境影响大、小目标检测困难以及漏检率高等问题,提出基于背景差分的机场跑道异物分块检测与跟踪算法.首先利用速度辅助的位置信息线性插值,获取背景模板图像库与待检测图像序列最相关帧;然后将图像分为不同子块,利用各子块内图像纹理复杂度设置自适应权值,结合ORB算法进行特征点提取,将各子块特征点归一化至原始图像,并与背景模板库最相关帧对齐作差,获取异物检测结果;最后引入核相关滤波器对检测结果进行多目标跟踪,采用各子块局部跟踪算法降低运算时间,并对跟踪结果进行可靠性检验.在3种实验场景下,与5种主流检测算法的对比实验结果表明,与目前已有的基于图像的机场异物检测算法相比,在保证算法处理速度的基础上,该算法将异物整体错检率降低了70%以上,并在异物尺寸大于1cm×1cm的情况下,将整体漏检率降低至0,获得了较好的效果.     

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。     

基于光照自适应动态一致性的无人机目标跟踪

无人机跟踪任务经常面临各种光线变化场景,然而无人机跟踪方法主要在光线充足下实现鲁棒跟踪。提出一种具有光照自适应性和跨帧语义感知动态一致性评估的无人机跟踪方法,实现光线不足下的无人机目标跟踪。首先构建光照自适应模块对昏暗场景进行识别,对视频图像的光照强度进行补偿;其次构建目标模板训练具有目标感知能力的滤波器进行相关运算,并利用跨帧之间的响应信息进行一致性评估;最后构建动态约束策略并对响应差异进行约束,使跟踪器保持时间平滑。在UAVDark135和UAV123数据集上,与9种先进算法进行对比实验,结果表明该算法具有较好的跟踪性能。     

适用于复杂环境下的实时目标跟踪技术

针对复杂环境下目标跟踪过程中目标存在旋转、视角、尺度等变化以及噪声干扰的问题,提出了一种基于尺度不变特征与快速模板匹配相结合的目标跟踪技术;该技术通过分别提取预先存储模板和实时采集图像的尺度不变特征,建立初始模板;采用菱形搜索策略对模板的低分辨率子图和待跟踪图像的低分辨率子图进行快速互相关检测,根据检测结果在该帧高分辨率图像中建立紧凑ROI,在此区域内进行模板匹配,对目标进行精确定位;在目标跟踪的过程中采用自适应模板更新策略,以保证在目标发生变化时跟踪的稳定性;实验结果表明,该技术在稳定性、准确性和实时性等方面均优于传统方法。     

动态云台摄像机无人机检测与跟踪算法

为应对小型无人机的黑飞、滥飞对个人隐私、公共安全造成的威胁,本文采用高清云台摄像机定点巡航的方式对近地动态复杂背景中的无人机进行检测与跟踪,并提出了一种适用于动态云台摄像机的闭环无人机检测与跟踪算法,包含检测与跟踪两种模式。在检测模式下,本文设计了一种基于运动背景补偿的运动目标检测算法来提取分类候选区域,然后利用基于神经网络结构搜索得到的轻量级卷积神经网络对候选区域进行分类识别,可在不缩小高清视频图像的条件下实现无人机检测;在跟踪模式下,本文提出了一种结合卡尔曼滤波的局部搜索区域重定位策略改进了核相关滤波跟踪算法,使之在高清云台伺服追踪过程中仍能对目标进行快速稳定的跟踪;为将检测模式与跟踪模式结合在闭环框架中,本文还提出了一种基于检测概率和跟踪响应图状态的自适应检测与跟踪切换机制。实验表明,本文算法可应用于定点巡航状态的高清云台摄像机,实现近地复杂动态背景中无人机的实时准确检测、识别与快速跟踪。     

适用于无人机的自动跟踪算法的研究

入侵农田问题给试验田的安全保护带来了严重挑战,传统的保护手段存在诸多限制。为解决这一问题,将无人机技术和目标检测与跟踪算法相结合,提出一种创新的解决方案。该方法通过无人机高空航拍视角获取农田图像数据,并利用YOLO(You Only Look Once)算法实现实时目标检测。同时,采用SORT(Simple Online and Realtime Tracking)算法对入侵目标进行持续跟踪。通过在海南试验田中的应用实验验证该方法的可行性和有效性。实验结果表明,基于YOLO和SORT算法的无人机目标检测与跟踪系统能够在0.4 s内快速检测和跟踪入侵农田目标,为试验田的安全保护工作提供了重要支持。     

利用深度卷积特征的无人机视觉跟踪

由于无人机视觉跟踪视角范围广且环境复杂,常遇到无人机飞行震动、目标遮挡、相似目标等问题,导致无人机跟踪目标发生漂移.因此,对具有回归计算的全卷积孪生网络跟踪算法(SiamRPN)进行改进,提出一种加强深度特征相关性的无人机视觉跟踪算法(SiamDFT).首先,将全卷积神经网络后三层卷积的网络宽度提升一倍,充分利用目标的外观信息,完成对模板帧和检测帧的特征提取;其次,在检测帧和模板帧分别提出注意力信息融合模块和特征深度卷积模块,两个深度的特征相关性计算方法能够有效抑制背景信息,增强像素对之间的关联性,高效完成分类和回归任务;然后,采用深度互相关运算完成相似性计算,并引入距离交并比的计算方法完成对目标的定位.实验结果表明, SiamDFT在无人机短时跟踪场景下精确率和成功率分别达到79.8%和58.3%,在无人机长时跟踪场景下精确率和成功率分别达到73.4%和55.2%,实景测试结果充分验证了所提出算法的有效性.     

基于改进PP-YOLO和Deep-SORT的多无人机实时跟踪算法

无人机（UAV）目标尺寸较小,多架无人机之间特征也不明显,且鸟类和飞虫的干扰给无人机目标的准确检测和稳定跟踪带来了巨大挑战。针对传统目标检测算法对小目标无人机检测性能差、跟踪不稳定的问题,提出一种基于改进PP-YOLO和Deep-SORT的多无人机实时跟踪算法。首先,将压缩-激励模块融入PP-YOLO检测算法中,以实现对无人机目标的特征提取和检测;其次,在ResNet50-vd结构中引入Mish激活函数,以解决反向传播过程中的梯度消失问题,并进一步提升检测精度;然后,采用Deep-SORT算法来实时跟踪无人机目标,并将提取外观特征的主干网络更换为ResNet50,从而改善原有网络对微小外观感知能力弱的状况;最后,引入损失函数Margin Loss,既提高了类别可分性,又加强了类内紧度和类间差异。实验结果表明,所提算法的检测平均精度均值（mAP）相比原始PP-YOLO算法提升了2.27个百分点,跟踪准确性相对于原始Deep-SORT算法提升了4.5个百分点。所提算法的跟踪准确性可达91.6%,能够实时跟踪600 m以内多架无人机目标,有效解决了跟踪过程中的“丢帧”问题。     

基于在线特征选择的实时多姿态人脸跟踪

针对复杂条件下的人脸跟踪问题, 将显著区域跟踪算法和基于 Adaboost 的人脸检测算法相结合, 研发了一个实时多姿态人脸跟踪系统. 系统采用数据关联结果, 自动选择和切换检测器与跟踪器, 并通过引入环境信息增强跟踪算法的稳定性. 实验表明, 系统可在目标姿态变化、摄像机运动等复杂条件下进行自动人脸检测与跟踪, 对 320x240 的图像序列处理速度达到 10-12帧/秒.     

基于多子块联合估计的相关滤波跟踪

针对遮挡情况下相关滤波算法跟踪精度下降的问题,提出了一种基于多子块联合估计的核相关滤波跟踪方法。首先依据初始帧跟踪框的几何特征对目标自适应分块,并采用KCF方法对各子块独立跟踪得到联合置信图;然后以上帧目标的位置及尺度作为先验信息对搜索区域采样,同时将样本框中置信图的权值密度作为观测值,利用粒子滤波算法实现候选目标的最优估计;最后对置信度较低的子块反向投影至上帧图像进行遮挡检测,防止模板错误更新。定性和定量实验结果表明,该方法与原始KCF算法相比跟踪精度提升约10%,具有良好的抗遮挡性,并对目标尺度变化具有一定的估计能力。     

在线多示例学习目标跟踪方法研究

多示例学习是不同于传统机器学习的一种新的学习模式,近年来被应用于图像检索、文本分类等领域。提出一种基于在线学习的多示例学习算法,将其应用于目标跟踪。该算法通过构造一个在线学习的多示例分类器作为检测器,无需制作大量的样本进行离线的训练,只需在第一帧手动选中目标,便可以自动生成正样本和负样本,并在随后的帧序列中,根据跟踪到的目标自动更新分类器,在跟踪器丢失目标或者目标从场景中消失后,它能够重新检测到目标并更新跟踪器,从而有效地支持了跟踪器跟踪目标。实验证明该方法在背景复杂,光线变化,摄像机抖动等复杂条件下,可以很好地跟踪到目标,且对遮挡具有较好的鲁棒性。     

基于自适应白化的音乐节拍实时跟踪算法*

提出一种基于自适应白化的音乐节拍实时跟踪算法。在对音乐信号进行触发点检测之前,构建音乐信号短时傅里叶变换的当前频域峰值表,对各频段的频谱幅值进行自适应加权,使各个频段保持相似的动态范围,改善音乐信号起伏变化较大时触发点检测准确性低的问题,进而提高节拍实时跟踪效果。在MIREX2006标准测试库上的节拍跟踪实验表明,自适应白化的引入可整体提高P-score,特别是对于起伏变化很大的音乐信号,节拍跟踪效果的提高非常明显。     

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。     

基于自适应特征融合的压缩感知跟踪算法

针对运动目标外观或背景变化较大时,采用基于压缩感知的跟踪算法由于特征单一易导致漂移、跟踪不稳定甚至丢失目标等问题,提出了改进的基于自适应特征融合的压缩感知跟踪算法。该算法采用两种随机测量矩阵,分别投影V、H空间得到压缩后的纹理和颜色特征,利用在线计算的特征可靠性相对程度来自适应调整特征加权系数,充分利用两类特征的互补性来增强跟踪稳定性。对不同视频的测试结果表明,提出的方法在目标外观、背景环境变化时仍能准确跟踪目标,在目标大小为70像素×100像素时平均帧率为22帧/s,达到实时性。与提取单一特征的原压缩感知算法相比,改进后的方法在目标外观和背景变化时具有更强的鲁棒性。     

Efficient multi-feature PSO for fast gray level object-tracking

Robust and real-time moving object tracking is a tricky job in computer vision systems. The development of an efficient yet robust object tracker faces several obstacles, namely: dynamic appearance of deformable or articulated targets, dynamic backgrounds, variation in image intensity, and camera (ego) motion. In this paper, a novel tracking algorithm based on particle swarm optimization (PSO) method is proposed. PSO is a population-based stochastic optimization algorithm modeled after the simulation of the social behavior of bird flocks and animal hordes. In this algorithm, a multi-feature model is proposed for object detection to enhance the tracking accuracy and efficiency. The object's model is based on the gray level intensity. This model combines the effects of different object cases including zooming, scaling, rotating, etc. into a single cost function. The proposed algorithm is independent of object type and shape and can be used for many object tracking applications. Over 30 video sequences and having over 20,000 frames are used to test the developed PSO-based object tracking algorithm and compare it to classical object tracking algorithms as well as previously published PSO-based tracking algorithms. Our results demonstrate the efficiency and robustness of our developed algorithm relative to all other tested algorithms.     

Mean shift tracker combined with online learning-based detector and Kalman filtering for real-time tracking

Color-based visual object tracking is one of the most commonly used tracking methods. Among many tracking methods, the mean shift tracker is used most often because it is simple to implement and consumes less computational time. However, mean shift trackers exhibit several limitations when used for long-term tracking. In challenging conditions that include occlusions, pose variations, scale changes, and illumination changes, the mean shift tracker does not work well. In this paper, an improved tracking algorithm based on a mean shift tracker is proposed to overcome the weaknesses of existing methods based on mean shift tracker. The main contributions of this paper are to integrate mean shift tracker with an online learning-based detector and to newly define the Kalman filter-based validation region for reducing computational burden of the detector. We combine the mean shift tracker with the online learning-based detector, and integrate the Kalman filter to develop a novel tracking algorithm. The proposed algorithm can reinitialize the target when it converges to a local minima and it can cope with scale changes, occlusions and appearance changes by using the online learning-based detector. It updates the target model for the tracker in order to ensure long-term tracking. Moreover, the validation region obtained by using the Kalman filter and the Mahalanobis distance is used in order to operate detector in real-time. Through a comparison against various mean shift tracker-based methods and other state-of-the-art methods on eight challenging video sequences, we demonstrate that the proposed algorithm is efficient and superior in terms of accuracy and speed. Hence, it is expected that the proposed method can be applied to various applications which need to detect and track an object in real-time.     

Multi-object tracking evaluated on sparse events

This article presents a visual object tracking method and applies an event-based performance evaluation metric for assessment. The proposed monocular object tracker is able to detect and track multiple object classes in non-controlled environments. The tracking framework uses Bayesian per-pixel classification to segment an image into foreground and background objects, based on observations of object appearances and motions in real-time. Furthermore, a performance evaluation method is presented and applied to different state-of-the-art trackers based on successful detections of semantically high level events. These events are extracted automatically from the different trackers an their varying types of low level tracking results. Then, a general new event metric is used to compare our tracking method with the other tracking methods against ground truth of multiple public datasets.     

A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways

Real-time highway traffic monitoring systems play a vital role in road traffic management, planning, and preventing frequent traffic jams, traffic rule violations, and fatal road accidents. These systems rely entirely on online traffic flow info estimated from time-dependent vehicle trajectories. Vehicle trajectories are extracted from vehicle detection and tracking data obtained by processing road-side camera images. General-purpose object detectors including Yolo, SSD, EfficientNet have been utilized extensively for real-time object detection task, but, in principle, Yolo is preferred because it provides a high frame per second (FPS) performance and robust object localization functionality. However, this algorithm’s average vehicle classification accuracy is below 57%, which is insufficient for traffic flow monitoring. This study proposes improving the vehicle classification accuracy of Yolo, and developing a novel bounding box (Bbox)-based vehicle tracking algorithm. For this purpose, a new vehicle dataset is prepared by annotating 7216 images with 123831 object patterns collected from highway videos. Nine machine learning-based classifiers and a CNN-based classifier were selected. Next, the classifiers were trained via the dataset. One out of ten classifiers with the highest accuracy was selected to combine to Yolo. This way, the classification accuracy of the Yolo-based vehicle detector was increased from 57% to 95.45%. Vehicle detector 1 (Yolo) and vehicle detector 2 (Yolo + best classifier), and the Kalman filter-based tracking as vehicle tracker 1 and the Bbox-based tracking as vehicle tracker 2 were applied to the categorical/total vehicle counting tasks on 4 highway videos. The vehicle counting results show that the vehicle counting accuracy of the developed approach (vehicle detector 2 + vehicle tracker 2) was improved by 13.25% and this method performed better than the other 3 vehicle counting systems implemented in this study.     

A 2D/3D model-based object tracking framework

This paper presents a robust framework for tracking complex objects in video sequences. Multiple hypothesis tracking (MHT) algorithm reported in (IEEE Trans. Pattern Anal. Mach. Intell. 18(2) (1996)) is modified to accommodate a high level representations (2D edge map, 3D models) of objects for tracking. The framework exploits the advantages of MHT algorithm which is capable of resolving data association/uncertainty and integrates it with object matching techniques to provide a robust behavior while tracking complex objects. To track objects in 2D, a 4D feature is used to represent edge/line segments and are tracked using MHT. In many practical applications 3D models provide more information about the object's pose (i.e., rotation information in the transformation space) which cannot be recovered using 2D edge information. Hence, a 3D model-based object tracking algorithm is also presented. A probabilistic Hausdorff image matching algorithm is incorporated into the framework in order to determine the geometric transformation that best maps the model features onto their corresponding ones in the image plane. 3D model of the object is used to constrain the tracker to operate in a consistent manner. Experimental results on real and synthetic image sequences are presented to demonstrate the efficacy of the proposed framework.     

基于稀疏卷积特征和相关滤波的实时视觉跟踪算法

为提高分层卷积相关滤波视觉跟踪算法的实时性能,提出一种稀疏卷积特征的实时目标跟踪算法。首先,在分析不同层卷积特征的基础上,采用等间隔采样的方式提取每个卷积层的稀疏卷积特征;然后,对每个卷积层特征的相关滤波响应值进行加权组合,得到目标预测的位置;最后,采用稀疏的模型更新策略进一步提高算法的运行速度。在OTB-2015新增的50组数据上对所提算法进行测试,实验结果表明,该算法的平均距离精度为82.2%,比原分层卷积特征跟踪算法提高了5.25个百分点,对目标姿态以及遮挡等变化具有较好的鲁棒性。该算法的平均跟踪速度为32.6帧/s,是原分层卷积特征跟踪算法的近3倍,能达到实时跟踪的效果。