一种高效的三维运动检索方法

随着运动捕获设备的普及,大量的运动数据可以直接得到,从而使得大规模的运动数据库的建立成为可能.在此背景下,研究以检索为核心的运动捕获数据处理技术就显得十分重要了.本文提出了一种对运动捕获数据中的人体的各个关节点提取一种基于三维空间变换规律的3D时空特征的方法,并基于时空运动连续性引入了关键空间的概念.针对各关节点时空特征相对保持独立的特性,本文用每个关节点作为索引,并通过数据驱动决策树的学习方法去分析关节点对运动相似的不同影响,最终实现了一个高效的运动检索系统.     

基于改进的数据驱动决策树分析的3维人体运动检索

随着大量3维人体运动捕获数据库的出现,使得如何对人体运动数据进行高效分析和处理,从而有效利用运动捕获数据库成为一个新的挑战。为了高效地进行3维人体运动检索,首先通过从人体运动中提取一种基于3维空间变换特征规律的空间变换特征和运动的一些关键的时间特性来得到人体运动的3维时序特征;然后针对不同的训练需求,通过改进的数据驱动决策树的学习方法来分析关节点对运动相似的不同影响,并在检索过程中按照不同影响程度依次对关键点进行相似度计算;最终实现了一个高效的运动检索仿真系统。     

基于三维特征提取的人体运动数据分析和检索

基于各关节点三维空间和时间关系的三维特征,提出了一种描述各关节点之间三维空间关系的时空特征,可以分别处理每一个关节点的局部时空特征。同时,三维时空特征避开与原始数据的直接接触,从而很大程度避免了维数灾难。实验结果显示,三维特征提高运动数据检索的效率和精度,可应用到运动语义智能分析等领域。     

无需人工干预的关节点提取和三维重建

随着现在人体的运动捕获和行为理解的研究的发展,对这项研究有了越来越高的要求。相对于原来的手动提取人体关节点作为特征点来研究,如何使得提取特征点更加自动化,对以后的运动捕获和行为理解的研究意义重大。提出一种在单目视觉条件下在第一帧自动提取人体关节点位置的方法,来解决传统的以手动标定提取人体关节点的问题,并且利用光流稀疏L_K算法 对提取出的关节点进行运动跟踪,得到运动人体二维坐标信息,结合像机模型通过几何计算获得人体关节点的深度信息。     

基于人体行为模型的跌倒行为检测方法

随着移动互联网的广泛应用, 智慧社区等一系列移动互联应用等得到人们的重视, 特别是以居家养老的老年人防跌倒检测备受关注. 针对目前老年人跌倒没有及时得到检测报警, 从而无法及时救助, 进而产生更严重的安全性的问题, 本文提出了一种跌倒检测方法. 本文提出的方法首先对特定人体进行扫描, 利用人体建模工具poser构建出人体模型, 在运动过程中根据关节点位置将二维坐标映射出相应的三维坐标并通过节点位置预测算法对映射后的三维坐标进行关节点位置预测, 然后将预测后的子关节聚合到父类三维空间坐标轴中并预测出父类关节点的运动状态, 当子关节点与父关节点预测结果同时处于跌倒状态, 则判断人体所处于跌倒状态. 由于所建立的运动模型在运动特征上具有较高的真实性, 以此获取关节点的数据变化真实可靠. 经过大量的实验数据表明, 本文提出的跌倒检测方法可以精准实时反应运动状态, 检测准确率为99%, 由此可见本文提出的方法应用于跌倒检测是有效并可靠的.     

基于人体姿势编码的运动数据检索

针对大规模运动捕获数据的检索问题,提出一种基于人体姿势编码的相似运动检索方法.首先将形成人体姿势的9段主要骨骼的运动空间划分成多个子空间,以各骨骼所处的子空间位置作为人体姿势的特征表示;然后根据特征表示对运动数据进行逐帧编码,将编码相同的连续帧划分成等价段;最后建立以等价段为基础的倒排索引来检索候选运动,并通过编码匹配...     

一种基于索引空间的三维运动捕获数据检索方法

为能有效检索当前大量的三维人体运动捕获数据从而投入使用,针对三维人体运动捕获数据的独有特性,提出一种高效的检索方法。首先通过关键帧提取对原始运动数据降维,然后采用自定义的符合人体运动语义的特征将原始角度数据转换为特征序列。根据数据库中所有运动片段对应的特征序列构建一种基于姿态特征的索引空间。检索时通过在索引空间上顺序匹配姿态特征来查找时序一致的匹配运动片段。实验结果表明,与大多数现有方法相比,该方法由于较好的运动语义特征提取,因此具有更好的时间效率,更高的灵活性和检索精度,能够满足多种运动数据的检索需要。     

一种基于运动姿态的三维人体运动检索方法

随着运动捕获技术的不断展,捕获出大量的三维人体运动数据并建立了大规模运动数据库。提出了一种基于运动姿态的三维人体运动检索方法(MPDS),首先进行姿态特征提取,其次通过特征编码对人体运动的局部姿态进行组合,以期达到更好地辨别力,并通过决策树学习方法得到局部运动姿态相似度对整体运动相似性的影响,以决策树指导检索过程。实验结果表明,文中算法的有效性。     

基于运动捕获文件的人体微多普勒信号仿真

针对传统人体微多普勒信号仿真算法适应模型简单、与人体相似度不高的问题,提出了一种基于运动捕获文件的仿真方法。该方法从运动捕获文件中获得各关节点的位置随时间的变化,根据关节点的相对位置建立由12个椭球体组成的人体三维模型,并根据此模型仿真RCS回波。此方法所建模型与实际人体外形相似度高,可对任意运动模式的人体雷达回波进行仿真。仿真结果验证了所提算法的正确性。     

面向人体动作识别的局部特征融合时间卷积网络

针对3D人体骨架序列动作识别这一问题,提出了一种结合了局部特征融合的时间卷积网络方法.首先,对一个动作中整个骨架序列的所有关节点的空间位置变化进行建模,提取其骨架序列的全局空间特征;然后,根据人体关节点及连接关系的拓扑结构将全局空间特征划分为人体局部空间特征,并将得到的局部空间特征分别作为对应TCN的输入,进而学习各关节内部的特征关系;最后,对输出的各部分特征向量进行融合,学习各部分关节之间的协作关系,从而完成对动作的识别.运用该方法在当前最具挑战性的数据集NTU-RGB+D进行了分类识别实验,结果表明,与已有的基于CNN,LSTM以及TCN的方法相比,其在对象交叉(cross-subject)和视图交叉(cross-view)的分类准确率上分别提高到了79.5%和84.6%.     

基于EMD距离的多示例聚类

多示例学习中,包由多个示例组成,有明确标记,而示例标记却不确定。已有聚类研究都针对单示例、单标记,因而无法直接应用于多示例问题。基于推土机距离(earth mover's distance, EMD)提出了一种新的多示例聚类算法ECMIL。该方法首先利用欧式距离计算包内示例相似度,将相似示例合并;然后将需要度量距离相似性的包内示例分别看作供货者和消费者,计算货物拥有量和货物需求量;对推土机距离无法供货问题,通过增大满足条件供货者的权值加以解决;最后使用k-mcdoids算法进行聚类。在基准数据集MUSK, Corcl和SIVAI上进行实验,表明EC-MIL算法是有效的。     

Compression of Human Motion Capture Data Using Motion Pattern Indexing

In this work, a novel scheme is proposed to compress human motion capture data based on hierarchical structure construction and motion pattern indexing. For a given sequence of 3D motion capture data of human body, the 3D markers are first organized into a hierarchy where each node corresponds to a meaningful part of the human body. Then, the motion sequence corresponding to each body part is coded separately. Based on the observation that there is a high degree of spatial and temporal correlation among the 3D marker positions, we strive to identify motion patterns that form a database for each meaningful body part. Thereafter, a sequence of motion capture data can be efficiently represented as a series of motion pattern indices. As a result, higher compression ratio has been achieved when compared with the prior art, especially for long sequences of motion capture data with repetitive motion styles. Another distinction of this work is that it provides means for flexible and intuitive global and local distortion controls.     

Multiple instance learning for classifying students in learning management systems

In this paper, a new approach based on multiple instance learning is proposed to predict student’s performance and to improve the obtained results using a classical single instance learning. Multiple instance learning provides a more suitable and optimized representation that is adapted to available information of each student and course eliminating the missing values that make difficult to find efficient solutions when traditional supervised learning is used. To check the efficiency of the new proposed representation, the most popular techniques of traditional supervised learning based on single instances are compared to those based on multiple instance learning. Computational experiments show that when the problem is regarded as a multiple instance one, performance is significantly better and the weaknesses of single-instance representation are overcome.     

基于PGA的人体运动捕获数据分割方法

由于人体运动捕获数据的固有非线性,线性方法并不总是能够有效地找到运动捕获数据的内在维度,针对这种情况,提出了基于主测地线分析（PGA）和概率主测地线分析（PPGA）的自动分割方法。这两种方法都将人体运动视为一个有序的姿势序列,并在姿势序列有局部变化处对运动进行分割。基于PGA的分割方法是在运动局部模型的内在维度突然增长处分配一个分割点,基于PPGA的分割方法是在姿势分布发生改变时放置分割点。实验结果表明,该方法都能实现自动分割,且具有较好的分割结果。     

基于多例学习的Web图像聚类

在图像分类和自动标注系统中,多例学习(MIL)是研究的热点.目前MIL中的算法多为监督学习方法.针对非监督学习,在基于EM算法和启发式迭代优化算法的框架下,提出了6种多例聚类算法,并通过它们对来自于真实Web环境下的图像进行聚类以分析用户的搜索兴趣.由于一幅图像含有若干个区域,每个区域可被看为一个样例,属于同一个图像的区域则组成一个包.因此如何理解图像语义内容的问题即转化为多例学习.在多例学习的经典数据集MUSK数据和来自于Web图像集上的比较实验表明,提出的多例聚类算法具有优良的聚类性能.     

Abnormal visual event detection based on multi-instance learning and autoregressive integrated moving average model in edge-based Smart City surveillance

The abnormal visual event detection is an important subject in Smart City surveillance where a lot of data can be processed locally in edge computing environment. Real-time and detection effectiveness are critical in such an edge environment. In this paper, we propose an abnormal event detection approach based on multi-instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places, focusing on real-time and detection effectiveness. We propose an unsupervised method for abnormal event detection by combining multi-instance visual feature selection and the autoregressive integrated moving average model. In the proposed method, each video clip is modeled as a visual feature bag containing several subvideo clips, each of which is regarded as an instance. The time-transform characteristics of the optical flow characteristics within each subvideo clip are considered as a visual feature instance, and time-series modeling is carried out for multiple visual feature instances related to all subvideo clips in a surveillance video clip. The abnormal events in each surveillance video clip are detected using the multi-instance fusion method. This approach is verified on publically available urban surveillance video datasets and compared with state-of-the-art alternatives. Experimental results demonstrate that the proposed method has better abnormal event detection performance for crowded scene of urban public places with an edge environment.     

人体跌倒识别算法研究

为快速有效地获取老人跌倒信息,提出了一种人体跌倒识别算法,以深度序列为基础,通过Kinect提供的骨架关节数据构建人体动作表示模型,将人体动作看作关节运动曲线和速度曲线的集合,引入离散Fréchet距离作为关节运动曲线和速度曲线的相似性测度,使用最近邻(KNN)分类器对动作样本进行分类.在公开数据集SDUFall上进行了实验,结果表明,方法优于已有方法.     

M<Emphasis Type="SmallCaps">l-rbf</Emphasis>: RBF Neural Networks for Multi-Label Learning

Multi-label learning deals with the problem where each instance is associated with multiple labels simultaneously. The task of this learning paradigm is to predict the label set for each unseen instance, through analyzing training instances with known label sets. In this paper, a neural network based multi-label learning algorithm named Ml-rbf is proposed, which is derived from the traditional radial basis function (RBF) methods. Briefly, the first layer of an Ml-rbf neural network is formed by conducting clustering analysis on instances of each possible class, where the centroid of each clustered groups is regarded as the prototype vector of a basis function. After that, second layer weights of the Ml-rbf neural network are learned by minimizing a sum-of-squares error function. Specifically, information encoded in the prototype vectors corresponding to all classes are fully exploited to optimize the weights corresponding to each specific class. Experiments on three real-world multi-label data sets show that Ml-rbf achieves highly competitive performance to other well-established multi-label learning algorithms.     

Few-experiential learning system of robotic picking task with selective dual-arm grasping

Recently, robots are introduced to warehouses and factories for automation and are expected to execute dual-arm manipulation as human does and to manipulate large, heavy and unbalanced objects. We focus on target picking task in the cluttered environment and aim to realize a robot picking system which the robot selects and executes proper grasping motion from single-arm and dual-arm motion. In this paper, we propose a few-experiential learning-based target picking system with selective dual-arm grasping. In our system, a robot first learns grasping points and object semantic and instance label with automatically synthesized dataset. The robot then executes and collects grasp trial experiences in the real world and retrains the grasping point prediction model with the collected trial experiences. Finally, the robot evaluates candidate pairs of grasping object instance, strategy and points and selects to execute the optimal grasping motion. In the experiments, we evaluated our system by conducting target picking task experiments with a dual-arm humanoid robot Baxter in the cluttered environment as warehouse.