Bayesian cluster detection and tracking using a generalized Cheeseman approach

2003 ◽  
Author(s):  
Ronald P. S. Mahler
Keyword(s):  
Cluster Detection ◽  
Detection And Tracking ◽  
Bayesian Cluster

scholarly journals Bayesian cluster detection via adjacency modelling

2016 ◽  
Vol 16 ◽  
pp. 11-20 ◽  
Author(s):  
Craig Anderson ◽  
Duncan Lee ◽  
Nema Dean
Keyword(s):  
Cluster Detection ◽  
Bayesian Cluster

Quantile regression and Bayesian cluster detection to identify radon prone areas

2016 ◽  
Vol 164 ◽  
pp. 354-364 ◽  
Author(s):  
Annalina Sarra ◽  
Lara Fontanella ◽  
Pasquale Valentini ◽  
Sergio Palermi
Keyword(s):  
Quantile Regression ◽  
Cluster Detection ◽  
Bayesian Cluster

scholarly journals FORMATION AND GROWTH OF CLUSTERS IN COLLOIDAL SUSPENSIONS

2007 ◽  
Vol 18 (04) ◽  
pp. 501-510 ◽  
Author(s):  
MARTIN HECHT ◽  
JENS HARTING ◽  
HANS J. HERRMANN
Keyword(s):  
Cluster Size ◽  
Md Simulation ◽  
Parallel Implementation ◽  
Colloidal Suspensions ◽  
Cluster Detection ◽  
Size Distributions ◽  
Simulation Algorithm ◽  
Detection And Tracking ◽  
Major Interest ◽  
First Results

Depending on the p H -value and salt concentration of Al 2 O 3 suspensions different microstructures can form. Especially the clustered one is of major interest for industrial purposes as found in the production of ceramics. In this paper we investigate the clustered microstructure by means of a coupled Stochastic Rotation Dynamics (SRD) and Molecular Dynamics (MD) simulation. In order to gain statistics within a system containing numerous clusters, large simulation volumes are needed. We present our parallel implementation of the simulation algorithm as well as a newly developed cluster detection and tracking algorithm. We then show first results of measured growth rates and cluster size distributions to validate the applicability of our method.

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

HUMAN FALL DETECTION AND TRACKING SYSTEM USING MEMS SENSOR

2017 ◽  
Vol 6 (3) ◽  
pp. 20
Author(s):  
A. SAIPRIYA ◽  
V. MEENA ◽  
MAALIK M.ABDUL ◽  
D. PRAVINRAJ ◽  
P. JEGADEESHWARI ◽  
...  
Keyword(s):  
Tracking System ◽  
Fall Detection ◽  
Mems Sensor ◽  
Detection And Tracking
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