基于视觉表情分析的交互式表情机器人系统研究

针对表情机器人双向情感交互的需求,从系统集成的角度提出了一种综合表情分析、识别及交互动作映射的表情机器人系统建模方案.首先,以开源Fritz仿人头部机器人为基础,通过扩展语音、视觉等功能模块,构建了表情机器人多通道情感表达映射模型.其次,为实现表情机器人与操作者的自然交互,提出以视觉分析处理作为情感分析及交互操作的核心,采用类Haar特征级联分类算法进行人脸自动定位检测,利用对光照和人脸姿态变化不敏感的Gabor特征作为情感表情特征并实施空域降采样.针对传统特征脸检测方法样本间分布不合理问题,提出结合类内PCA分析、能量维数及特征重构来筛选特征向量,并张量成特征空间完成表情的分类识别.最后,在JAFFE库及实时视频图片上进行的测试结果证实了该系统建模方案的可行性和有效性.

A parallel algorithm for detecting traffic patterns using stay point features and moving features

In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the features of the stay points in different traffic patterns are extracted, that is, the stay points of various traffic patterns are identified, respectively, and the clustering algorithm is used to mine the unique features of the stop points to different traffic patterns. Then, the moving features in different traffic patterns are extracted from a trajectory of a moving object, including the maximum speed, the average speed, and the stopping rate. A classifier is constructed to predict the traffic pattern of the trajectory using the stay points and moving features. Finally, a parallel algorithm based on Spark is proposed to detect traffic patterns. Experimental results show that the stay points and moving features can reflect the difference between different traffic modes to a greater extent, and the detection accuracy is higher than those of other methods. In addition, the parallel algorithm can increase the speed of identifying traffic patterns.

时空轨迹分类研究进展

时空轨迹分类旨在为一条轨迹预测类别。时空轨迹分类在城市规划、个性化用户推荐等方面具有重要应用价值,其过程主要包括轨迹数据预处理、特征提取、建立分类器3个阶段。本文综述了近年来时空轨迹分类的研究进展,首先对时空轨迹分类的过程进行概述;然后将时空轨迹分类算法按特征提取的方式分为基于运动特征的轨迹分类算法、基于分类规则的轨迹分类算法和基于图像信号分析的轨迹分类算法3类,分别论述了这些算法的基本思想和优缺点;之后对现有的轨迹分类算法从数据来源、分类器、特征提取方式等方面进行对比分析;最后讨论现有的时空轨迹分类算法面临的挑战。