2017 International Conference on Progress in Informatics and Computing (PIC)最新文献_第9页

Fast non-blind deconvolution method for blurred image corrupted by poisson noise 泊松噪声破坏模糊图像的快速非盲反卷积方法

2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359539

Shuyin Tao, Wen-de Dong, Zhenmin Tang, Qiong Wang

引用次数: 4

An intelligent old-age home endowment monitoring system based on Internet of Things 一种基于物联网的智能养老监控系统

2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359568

Xian-xian Ma, X. Zhang, Li-xin Guo, Zhen-wei Ding, Li-long Zhang, S. Wei, Rong Fan, Yuanze Ma

引用次数: 1

A novel K-means classification method with genetic algorithm 基于遗传算法的k -均值分类新方法

2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359511

Xuesi Li, Kai Jiang, Hongbo Wang, Xuejun Zhu, Ruochong Shi, Haobin Shi

引用次数: 2

Specialization or generalization: A study on breadth-first graph traversal on GPUs 专门化或泛化:gpu上宽度优先图遍历的研究

2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359560

Wenyong Zhong, Yanxin Cao, Jiawen Li, Jianhua Sun, Hao Chen

引用次数: 1

An energy-aware mapping algorithm for mesh-based network-on-chip architectures 基于网格的片上网络架构的能量感知映射算法

2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359572

Jin Sun, Yi Zhang

引用次数: 3

Distribution law of user comments on hot news 热点新闻用户评论的分布规律

2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359592

Hong Zong, Xiuzhi Wu, Chunxiang Xue, Fen Chen

引用次数: 0

An object tracking method using deep learning and adaptive particle filter for night fusion image 基于深度学习和自适应粒子滤波的夜间融合图像目标跟踪方法

2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359530

Xiaoyan Qian, Lei Han, Yanlin Zhang, M. Ding

引用次数: 4

Research on abnormal behavior target tracking algorithm in airport intelligent video surveillance 机场智能视频监控中异常行为目标跟踪算法研究

2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359533

Daihao Zhang, Xiaoyan Qian, Yanlin Zhang

{"title":"Research on abnormal behavior target tracking algorithm in airport intelligent video surveillance","authors":"Daihao Zhang, Xiaoyan Qian, Yanlin Zhang","doi":"10.1109/PIC.2017.8359533","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359533","url":null,"abstract":"With the rapid development of China's civil aviation industry, the airport is facing increasing pressure on security. In this paper, the target tracking algorithm in Intelligent Video Surveillance (IVS) is studied. It aims to provide ideas and reference for the development and implementation of high performance intelligent video surveillance system. The main contents of this paper are as follows: Aiming at the problem of tracking failure caused by occlusion, deformation and illumination changes, this paper proposes a target tracking algorithm that combines the apparent features and depth characteristics. Firstly, the CNN network is trained by a large number of pedestrian databases, and then the depth characteristics of the target area are extracted by trained CNN network. At the same time, the color histogram of the target area in HSV space is calculated, and the depth feature and color feature are combined to get the whole feature. Finally, a number of hypothetical states are estimated under the framework of particle filter, the optimal position of the target is obtained, the tracking result is obtained, and the template is updated. Finally, the resampling is carried out according to the degeneration of the particle. Experiments show that the tracking algorithm has good tracking robustness. Finally, the target tracking system is designed and simulated on the Matlab platform. The validity and practicability of the algorithm are verified.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130348115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Efficient pose machine based on parameter-sensitive hashing 基于参数敏感哈希的高效姿态机

2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359590

Shan Lin, Bowen Liu, Yang Wen, Anum Masood, Bin Sheng, P. Li, Xin Liu, Haoyang Yu, Weiyao Lin

引用次数: 0

Multimodal deep learning network based hand ADLs tasks classification for prosthetics control 基于多模态深度学习网络的手部ADLs任务分类假肢控制

2017 International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2017-12-01 DOI: 10.1109/PIC.2017.8359521

L. Zhengyi, Zhou Hui, Yang Dandan, Xie Shui-qing

{"title":"Multimodal deep learning network based hand ADLs tasks classification for prosthetics control","authors":"L. Zhengyi, Zhou Hui, Yang Dandan, Xie Shui-qing","doi":"10.1109/PIC.2017.8359521","DOIUrl":"https://doi.org/10.1109/PIC.2017.8359521","url":null,"abstract":"Natural control methods based on surface electromyography (sEMG) and pattern recognization are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many activities of daily living (ADLs). Difficulty results from limited sEMG signals susceptible to interference in clinical practice, it needs to synthesize hand movement and sEMG to improve classification robustness. Human hand ADLs are made of complex sequences of finger joint movements, and capturing the temporal dynamics is fundamental for successful hand prosthetics control. Current research suggests that recurrent neural networks (RNN) are suited to automate feature extraction for time series domains, and dynamic movement primitives (DMP) can provide representation of hand kinematic primitives. We design a multimodal deep framework for inter-subject ADLs recognization, which: (i) implements heterogeneous sensors fusion; (ii) does not require hand-crafted features; and (iii) contains the dynamics model of the hand ADLs task control. We evaluate our framework on Ninapro datasets. Results show that our framework outperforms competing deep networks with single modal and some of the previous reported results.","PeriodicalId":370588,"journal":{"name":"2017 International Conference on Progress in Informatics and Computing (PIC)","volume":"50 1-5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123447969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5