Feedback-based object detection for multi-person pose estimation

In this paper, a novel method is proposed for increasing the performance through coupling of top-down models adjusting the object detector based on a new loss function. Generally, object detectors and keypoint estimators are sequentially used in real-time multi-person pose estimations; however, these two models are separately trained. Therefore, the results of the object detector are not optimized for the keypoint estimator. To solve this problem, we analyze the relationship between the two models and propose a feedback-based loss optimization in the object detector, based on the estimation results of the keypoint estimator. In addition, the resulting bounding box of the object detector is readjusted to improve the accuracy of the keypoint estimation model. The experimental results demonstrate that the proposed approach can perform real-time operations with a high frame rate similar to that of the baseline model. Moreover, it achieved an accuracy of 74.2 average precision (AP), which is higher than the state-of-the-arts model including the human detector used in the experiment.     

Sentiment Analysis based Multi-Person Multi-criteria Decision Making methodology using natural language processing and deep learning for smarter decision aid. Case study of restaurant choice using TripAdvisor reviews

Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via the procedure named criteria weighting through the attention of the experts. We evaluate the methodology in a case study of restaurant choice using TripAdvisor reviews, hence we build, manually annotate, and release the TripR-2020 dataset of restaurant reviews. We analyze the SA-MpMcDM methodology in different scenarios using and not using natural language and numerical evaluations. The analysis shows that the combination of both sources of information results in a higher quality preference vector.     

基于卷积神经网络的多人行为识别方法

为了解决多人行为识别中人物角色多且难以区分、图片增加的特征维数难以表达和学习以及行为背景复杂且容易产生干扰等问题,提出了一种基于卷积神经网络的多人行为识别方法。考虑到多人行为识别的复杂性,选择较为容易的两人交互行为作为研究对象,对实验中需要的图像数据库进行了初步的收集与预处理;然后选用在特征提取中不受拍摄角度、光照强度影响的Dense-sift算法来对原始图像进行初步的特征提取。由于人体行为图片相对手写数字图片更为复杂,因此为了使该网络能够很好地 识别 人体行为,针对该网络在其输入、网络层数、滤波器核数、学习率、输出等方面进行了修改。实验结果表明,提出的方法对拳击、拥抱、接吻3类交互行为的识别是有效的。     

Uncertain induced aggregation operators and its application in tourism management

We develop a new decision making approach for dealing with uncertain information and apply it in tourism management. We use a new aggregation operator that uses the uncertain weighted average (UWA) and the uncertain induced ordered weighted averaging (UIOWA) operator in the same formulation. We call it the uncertain induced ordered weighted averaging - weighted averaging (UIOWAWA) operator. We study some of the main advantages and properties of the new aggregation such as the uncertain arithmetic UIOWA (UA-UIOWA) and the uncertain arithmetic UWA (UAUWA). We study its applicability in a multi-person decision making problem concerning the selection of holiday trips. We see that depending on the particular type of UIOWAWA operator used, the results may lead to different decisions.     

多人协作虚拟实验室综述

从多人协作虚拟实验室的需求、特征、模型和结构以及系统关键技术这几个方面对多人协作虚拟实验室的研究情况进行综述,分析比较了CVL(Collaborative Virtual Laboratory)典型系统的特征,讨论了CVL系统的关键技术,最后提出了CVL系统存在的问题和进一步的发展方向,这对多人协作虚拟实验室系统的开发与研究具有一定的参考价值。     

采集多人重叠图像中高精度分割方法研究

研究随机采集多人重叠图像的分割问题,提高多人重叠图像分割完整性.随机采集的图像受到人员不可控的影响,在采集过程中很容易出现人员的重叠.传统的多人重叠图像分割,评估采用主观评估的方法,需要对图像质量进行评估后,再优化分割图像效果,加入了固定的前提条件,对随机采集图像的分割受到限制.提出一种基于计算机图形学的随机采集多人重叠图像的分割方法,通过计算机视觉识别技术,对多人重叠图像进行分割的奇异值特征提取,解决随机性的问题,提取多人重叠图像主要奇异值特征,进行图像质量评估,优化后期的评估效果.仿真结果表明,采用计算机图形学识别方法分割提取图像特征像点进行多人重叠图像质量评估,整体效果可以得到大幅提高.     

基于多人决策理论的船舶辐射噪声识别

船舶辐射噪声的识别非常复杂,为提高其分类器的分类性能和可靠性．提出一种基于多人决策理论的多分类器决策模型和算法．通过采用Welch谱、线性预测编码谱和Burg谱3种特征对应的BP神经网络和支持向量机分类器组成的6个分类结果进行群体决策,并对海上实测的3类目标辐射噪声数据进行分类．实验结果表明,对3类目标的总体正确识别概率达到96．29％．比单个分类器具有更好的分类性能．