一种新的基于时空马尔可夫随机场的运动目标分割技术

在图像处理领域,视频图像序列中的运动目标分割技术是一个被广泛研究的热点课题。该文提出一种新的基于时空马尔可夫随机场的运动目标分割技术。首先,对视频序列的前后3帧图像进行处理,获得两帧初始标记场;随后,对两帧初始标记场进行“与”操作,获得共同标记场;最后,以原始图像的色彩聚类图像作为先验知识,重新定义Gibbs能量函数,并利用迭代条件模型（ICM）实现最大后验概率（MAP）的估算问题,获得优化标记场。实验结果表明：该模型克服了传统时穿马尔可夫随机场模型因运动产生的晶露遮挡现象,同时减弱了运动一致性造成的空洞现象并削弱了噪声的影响。     

基于粒子群算法和卡尔曼滤波的运动目标跟踪算法

针对目前一些常用的运动目标跟踪算法存在跟踪精度不高、实时性低、对遮挡问题处理不佳等问题,提出一种粒子群算法与卡尔曼滤波相结合的新的运动目标跟踪方法。利用卡尔曼滤波预测目标中心在下一帧图像中的位置,从而极大减少了搜索范围,并以该位置为中心建立目标搜索区域。然后以目标的灰度统计特征对目标模板和候选区域进行匹配,确保跟踪准确性。为了有效减少搜索匹配次数、提高实时性,利用粒子群算法在搜索区域找到和目标模板最相似的区域,从而找到最优中心位置,并以该位置作为卡尔曼滤波的观测值,进行下一帧跟踪。仿真实验结果表明新算法显著提高了跟踪的实时性、精确性,并对部分遮挡能较好地处理。     

HOS运动目标分割算法在视频监控中的应用

为了提高视频监控中运动目标分割的速度和准确度,研究并实现了一种基于高阶统计量HOS(HigherOrder Statistics)的分割算法.首先根据HOS假设检验处理帧差图,判定像素点是否属于运动区域,阈值通过灰度共生矩阵获得,考虑了背景纹理的慢变化.然后,用矩形框聚类法大致确定运动目标的范围,在该范围内使用形态运算法和首尾扫描法去除空洞.最后,使用模板相与法获得帧图像的运动目标模板,从原图像中分割运动区域.算法采用了由粗到精的分析策略,实验表明,是一种快速稳健的算法.     

基于灰度特征和自适应阈值的虚拟背景提取研究

针对虚拟背景提取(Visual Background extractor,ViBe)算法在目标检测时容易出现鬼影和运动目标阴影的缺点,该文提出了一种基于灰度特征和自适应阈值的ViBe背景建模改进方法。该算法首先利用ViBe算法进行背景建模,得到前景目标,然后对前景目标进行灰度特征判断和自适应阈值比较,得到没有鬼影和运动目标阴影的运动目标。实验结果表明,改进后的算法可以很好地弥补ViBe算法的不足,提高ViBe算法的识别准确率。     

Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking

A new algorithm meant for content based image retrieval (CBIR) and object tracking applications is presented in this paper. The local region of image is represented by local maximum edge binary patterns (LMEBP), which are evaluated by taking into consideration the magnitude of local difference between the center pixel and its neighbors. This LMEBP differs from the existing LBP in a manner that it extracts the information based on distribution of edges in an image. Further, the effectiveness of our algorithm is confirmed by combining it with Gabor transform. Four experiments have been carried out for proving the worth of our algorithm. Out of which three are meant for CBIR and one for object tracking. It is further mentioned that the database considered for first three experiments are Brodatz texture database (DB1), MIT VisTex database (DB2), rotated Brodatz database (DB3) and the fourth contains three observations. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and other existing transform domain techniques.     

Optimizing Deep Learning Parameters Using Genetic Algorithm for Object Recognition and Robot Grasping

The performance of deep learning (DL) networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm (GA) based deep belief neural network (DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-and-place operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.     

视频监控系统中一种运动目标的检测方法

提出了一种基于改进的背景差分法的运动目标检测和识别的方法,该算法用于视频监控系统中运动目标检测和报警。双阈值法和动态阈值法有效地检测出图像中的运动目标。Matlab 7.0中对算法进行了仿真,实验表明,该方法有效去除了运动目标阴影及背景噪声,可准确地检测出运动目标。     

Human Action Recognition Based on Dense Trajectories Analysis and Random Forest

This paper presents a human action recognition method. It analyzes the spatio-temporal grids along the dense trajectories and generates the histogram of oriented gradients (HOG) and histogram of optical flow (HOF) to describe the appearance and motion of the human object. Then, HOG combined with HOF is converted to bag-of-words (BoWs) by the vocabulary tree. Finally, it applies random forest to recognize the type of human action. In the experiments, KTH database and URADL database are tested for the performance evaluation. Comparing with the other approaches, we show that our approach has a better performance for the action videos with high inter-class and low inter-class variabilities.     

Moving Query Monitoring in Spatial Network Environments

Moving queries over mobile objects are an important type of query in moving object database systems. In recent years, there have been quite a few works in this area. Due to the high frequency in location updates and the expensive cost of continuous query processing, server computation capacity and wireless communication bandwidth are the two limiting factors for large-scale deployment of moving object database systems. Many techniques have been proposed to address the server bottleneck including one using distributed servers. To address both scalability factors, distributed query processing techniques have been considered. These schemes enable moving objects to participate in query processing to substantially reduce the demand on server computation, and wireless communications associated with location updates. Most of these techniques, however, assume an open-space environment. Since Euclidean distance is different from network distance, techniques designed specifically for an open space cannot be easily adapted for a spatial network. In this paper, we present a distributed framework which can answer moving query over moving objects in a spatial network. To illustrate the effectiveness of the proposed framework, we study two representative moving queries, namely, moving range queries and moving k-nearest-neighbor queries. Detailed algorithms and communication mechanisms are presented. The simulation studies indicate that the proposed technique can significantly reduce server workload and wireless communication cost.     

基于一种卷积神经网络的目标快速跟踪技术

特定动态目标的快速检测及跟踪,是计算机视觉领域重要的课题。改变特征图在YOLOv3卷积神经网络中的选取位置,通过收集相关网络数据(类似模式分析、统计建模和计算学习视觉对象类别数据集合,即PASCAL Visual Object Classes数据集)构建自定义数据集合进行训练,使用面积的交并比完成辅助类别的联合,构建了能够实时检测特定目标在相关可视对象类检测数据集合上mAP@75达到47.41的检测器。联合卡尔曼滤波和匈牙利算法,通过将面积信息加入到匈牙利算法的代价矩阵中,改善了使用原方法产生大量ID切换(ID switch)的问题。该方法满足快速识别与跟踪的要求,在使用一张NVIDIA GeForce GTX 1060 6GB GPU条件下,平均速度能达到0.109 7 s/帧。     

微动复杂目标雷达散射截面计算方法

在对雷达目标的动态特征提取和识别的研究中,需要能够逼真地反映目标的结构特性和运动特性的模拟信号。文中应用物理光学法和物理绕射理论,针对复杂动目标和多目标对目标面元和尖劈进行多普勒频率分级的方法实现了动目标的雷达散射截面计算;提出了微面元快速消隐算法和微动目标的模型剖分准则。应用该方法可以模拟转动、颤动以及非刚体运动目标的电磁散射特性。     

基于背景差分和混合帧差的运动目标检测

针对利用核密度估计建立背景模型时计算量大,运动目标和外界环境容易发生变化,提出一种基于改进的核密度估计背景差分法和改进的混合帧差法相结合的运动目标检测方法。该方法在背景建模时,先对背景差分后的图像进行分块和分类,并简化了核密度估计的核函数,对前景块中的像素进行核密度估计,减少了计算量。在混合帧差法中增加了动态阈值,提高了对光线变化的适应性。实验结果表明该方法能够完整地提取出运动目标,提高了目标检测的准确率。     

Tracking deformable and occluded objects using particle filtering and GVF-Snake

An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by using the background differencing method, and the true contours of objects can be con-verged by means of the powerful searching ability of a modified GVF-Snake. Finally, an Energetic Particle Filtering (EPF) algorithm is obtained by combining particle filtering and a modified GVF-Snake, and by us-ing K-means and the EPF algorithm, multiple objects can be tracked. The proposed tracking tactic for par-tially occluded objects can effectively improve its anti-occlusion ability. Experiments show that this algorithm can obtain better tracking effect even though the tracked object is occluded.     

基于轨迹分析的交通目标异常行为识别

针对交通监控中运动目标的异常行为识别问题,提出一种基于轨迹分析的异常行为识别方法。首先,引入目标的空间位置、运动速度、运动方向和大小尺寸等特征参数对轨迹进行描述和聚类,以提高对目标轨迹的区分和识别能力;然后,提出一种行为识别数据库的建立和调用方法,并以实际交通场景为例,详细说明了数据库的建立和调用过程;最后,采用基于Bayes最优化的方法对轨迹进行联合匹配和边缘匹配,并根据匹配情况调用行为识别数据库对目标行为进行识别。实验结果表明,该方法切实有效,具有一定的实际应用价值。     

Oracle系统中面向对象技术的应用

关系数据库与面向对象技术的结合产生了面向对象的数据库技术，它的出现使传统的关系数据库功能上得到了进一步扩充。文中介绍了Oracle系统中面向对象的技术，并从实际应用出发，讨论了它的应用方法。     

时空道路网络连续最近邻查询的新方法

时空道路网络里连续最近邻查询在时空数据库查询的领域是一个新的重要课题，本文提出一个有效的方法（IE）以处理道路网络里的移动对象的k个最近邻居（k-NN）查询问题。     

基于粒子滤波的空-地目标跟踪算法

针对空-地目标跟踪中目标大幅度变速运动而引 起的跟踪失败问题,基于Kristan等人提出的双步(TS)动态模型框架,对空-地目标跟 踪中目标运动特点进行分析与建模,改进TS模型中 的保守模型以适应加速运动,提出适于描述大幅度变速运动的加速度双步(TSA)动态模型作 为粒子滤波(PF)跟踪算法的动态模 型,实现对粒子状态的精确预测,进而达到使用较少粒子即可对目标鲁棒跟踪的目的。对空 -地目标跟踪的测试视频进行测 试,结果表明,本文算法可对大幅度变速运动目标稳定跟踪,正确跟踪率为92%,对目标 尺寸约为25pixel×30pixel时的处理帧率为29frame/s。本文算法具有较好的鲁棒性与实时性。     

Video object tracking using adaptive Kalman filter

In this paper, a new video moving object tracking method is proposed. In initialization, a moving object selected by the user is segmented and the dominant color is extracted from the segmented target. In tracking step, a motion model is constructed to set the system model of adaptive Kalman filter firstly. Then, the dominant color of the moving object in HSI color space will be used as feature to detect the moving object in the consecutive video frames. The detected result is fed back as the measurement of adaptive Kalman filter and the estimate parameters of adaptive Kalman filter are adjusted by occlusion ratio adaptively. The proposed method has the robust ability to track the moving object in the consecutive frames under some kinds of real-world complex situations such as the moving object disappearing totally or partially due to occlusion by other ones, fast moving object, changing lighting, changing the direction and orientation of the moving object, and changing the velocity of moving object suddenly. The proposed method is an efficient video object tracking algorithm.     

非高斯条件下基于粒子滤波的T波交替检测算法

针对动态心电图中非稳态、非线性和非高斯T波交替检测（T-wave alternans,TWA）的稳健检测问题,本文首先提出了一个TWA的非线性、拉普拉斯噪声的状态空间模型,然后采用粒子滤波方法对动态心电信号中的T波交替幅值进行估计,实现了心电信号中T波交替的定性和定量分析.仿真结果表明,在加入高斯噪声和非高斯噪声情况下,与经典的SM和MMA方法进行对比,本文提出的算法准确度和稳健性表现最好,并且本文算法能准确地实时地跟踪非稳态的TWA现象.     

基于分形IFS的运动目标RCS序列分解

针对运动目标ＲＣＳ变化规律,提出将目标ＲＣＳ变化序列分解为确定变化序列和随机变化序列的设想,其中ＣＶＳ敏感于姿态,在姿态域表现为丰富的细节特征,而ＲＶＳ则表现为近似正态分布的非平稳随机过程,本文用实验数据证明这两邓列均具有分形的物生理上发形迭代函数系统将这两种变化序列进行有效分解,其儿效果明显优于传统的滤波方法