Autonomous crowds tracking with box particle filtering and convolution particle filtering

DOI10.1016/j.automatica.2016.03.009zbMath1338.93369arXiv1601.02429OpenAlexW2255760501WikidataQ59265047 ScholiaQ59265047MaRDI QIDQ286350

Amadou Gning, Donka Angelova, Lyudmila Mihaylova, Allan De Freitas, Visakan Kadirkamanathan

Publication date: 20 May 2016

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1601.02429

zbMATH Keywords

box particle filterconvolution particle filtercrowd tracking

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Control/observation systems with incomplete information (93C41) Estimation and detection in stochastic control theory (93E10) Automated systems (robots, etc.) in control theory (93C85)

Related Items (2)

A box regularized particle filter for state estimation with severely ambiguous and non-linear measurements ⋮ Multi-vehicle tracking with microscopic traffic flow model-based particle filtering

Cites Work

• Unnamed Item
• Unnamed Item
• The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking
• Enhanced consistency of the resampled convolution particle filter
• Non parametric distributed inference in sensor networks using box particles messages
• Robust set-membership state estimation; application to underwater robotics
• Nonlinear bounded-error state estimation of continuous-time systems
• Box particle filtering for nonlinear state estimation using interval analysis
• Sequential Monte Carlo Methods in Practice
• Bernoulli Particle/Box-Particle Filters for Detection and Tracking in the Presence of Triple Measurement Uncertainty

This page was built for publication: Autonomous crowds tracking with box particle filtering and convolution particle filtering