中国科学院机构知识库网格系统: 检索

Particle Filter Reinforcement via Context-Sensing for Smartphone-Based Pedestrian Dead Reckoning 期刊论文 OAI收割
IEEE COMMUNICATIONS LETTERS, 2021, 卷号: 25, 期号: 9, 页码: 3144-3148
作者: Shao, Wenhua; Zhao, Fang; Luo, Haiyong; Tian, Hui; Li, Jiaxin

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收藏 | 浏览/下载：45/0 | 提交时间：2021/12/01

Particle filters Neural networks Reinforcement learning Mathematical model Particle measurements Estimation Atmospheric measurements Indoor location tracking particle filter pedestrian dead reckoning reinforcement learning smartphone-based navigation

Assessing the contribution of groundwater to catchment travel time distributions through integrating conceptual flux tracking with explicit Lagrangian particle tracking 期刊论文 OAI收割
ADVANCES IN WATER RESOURCES, 2021, 卷号: 149, 页码: 18
作者: Jing, Miao; Kumar, Rohini; Attinger, Sabine; Li, Qi; Lu, Chunhui

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收藏 | 浏览/下载：27/0 | 提交时间：2021/05/25

Travel time distribution Flux tracking Particle tracking Coupled model Predictive uncertainty

Scale-dependent nonequilibrium features in a bubbling fluidized bed 期刊论文 OAI收割
AICHE JOURNAL, 2018, 卷号: 64, 期号: 7, 页码: 2364, 2378
作者: Wang, HF; Chen, YP; Wang, W; Wang, Haifeng; Chen, Yanpei

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收藏 | 浏览/下载：37/0 | 提交时间：2018/12/29

experiment GAS-SOLID FLOWS particle tracking velocimetry (PTV) NEAR-WALL REGION gas-solid fluidization VELOCITY DISTRIBUTIONS meso-scale PARTICLE-MOTION spatio-temporal structure MULTISCALE CFD GRANULAR FLOWS KINETIC-THEORY MODEL SIMULATION RISER

Giant jellyfish Nemopilema nomurai gathering in the Yellow Sea-a numerical study 期刊论文 OAI收割
JOURNAL OF MARINE SYSTEMS, 2015, 卷号: 144, 页码: 107-116
作者: Wei, Hao; Deng, Lijing; Wang, Yuheng; Zhao, Liang; Li, Xia

收藏 | 浏览/下载：33/0 | 提交时间：2015/06/15

Giant jellyfish Nemopilema nomurai Fall gathering Inter-annual variation Particle tracking model Yellow Sea

Contribution of the Karimata Strait transport to the Indonesian Throughflow as seen from a data assimilation model 期刊论文 OAI收割
CONTINENTAL SHELF RESEARCH, 2015, 卷号: 92, 页码: 16-22
作者: He, Zhigang; Feng, Ming; Wang, Dongxiao; Slawinski, Dirk

收藏 | 浏览/下载：27/0 | 提交时间：2016/11/02

South China Sea Numerical model Particle tracking Indonesian Throughflow

Contribution of the Karimata Strait transport to the Indonesian Throughflow as seen from a data assimilation model 期刊论文 OAI收割
CONTINENTAL SHELF RESEARCH, 2015, 卷号: 92, 页码: 16-22
作者: He, Zhigang; Feng, Ming; Wang, Dongxiao; Slawinski, Dirk

收藏 | 浏览/下载：24/0 | 提交时间：2016/11/02

South China Sea Numerical model Particle tracking Indonesian Throughflow

Robust visual tracking of infrared object via sparse representation model 会议论文 OAI收割
International Symposium on Optoelectronic Technology and Application 2014, Beijing, China, May 13-15, 2014
作者: Ma JK(马俊凯); Luo HB(罗海波); Chang Z(常铮); Hui B(惠斌)

收藏 | 浏览/下载：26/0 | 提交时间：2014/12/29

Sparse representation Target tracking Appearance model Robust tracking Particle lter

Real-Time Probabilistic Covariance Tracking With Efficient Model Update 期刊论文 OAI收割
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2012, 卷号: 21, 期号: 5, 页码: 2824-2837
作者: Wu, Yi; Cheng, Jian; Wang, Jinqiao; Lu, Hanqing; Wang, Jun

收藏 | 浏览/下载：33/0 | 提交时间：2015/08/12

Covariance descriptor incremental learning model update particle filter Riemannian manifolds visual tracking

The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China
作者: Sun H.; Han H.-X.; Sun H.

收藏 | 浏览/下载：56/0 | 提交时间：2013/03/25

Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

3D Model Based Vehicle Tracking Using Gradient Based Fitness Evaluation under Particle Filter Framework 会议论文 OAI收割
Istanbul, Turkey, 23-26 August 2010
作者: Zhaoxiang Zhang; Kaiqi Huang; Tieniu Tan; Yunhong Wang

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收藏 | 浏览/下载：12/0 | 提交时间：2017/02/09

Particle Filter Model Based Tracking Fitness Evaluation