Real-time lane tracking using Rao-Blackwellized particle filter

2012 ◽  
Vol 11 (1) ◽  
pp. 179-191 ◽  
Author(s):  
Marcos Nieto ◽  
Andoni Cortés ◽  
Oihana Otaegui ◽  
Jon Arróspide ◽  
Luis Salgado
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Lane Tracking

scholarly journals Real Time Color Object Tracking on Cell Broadband Engine Using Particle Filters

2010 ◽  
Vol 14 (3) ◽  
pp. 272-280 ◽  
Author(s):  
Norikazu Ikoma ◽  
◽  
Akihiro Asahara ◽  
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Real Time ◽  
Visual Tracking ◽  
Single Object ◽  
Cell Broadband Engine ◽  
Time Performance ◽  
Large Size ◽  
Particle Filter Algorithm ◽  
Local Store

Real time visual tracking by particle filter has been implemented on Cell Broadband Engine in parallel. Major problem for the implementation is small size of Local Store (LS) in SPEs (Synergistic PEs), which are computational cores, to deal with image of large size. As a first step for the implementation, we focus on color single object tracking, which is one of the most simple case of visual tracking. By elaborating to compress the color extracted image into bit-wise representation of binary image, all information of the color extracted image can be stored in LS for 640×480 size of original image. By applying our previous implementation of general particle filter algorithm on Cell/B.E. to this specific case, we have achieved real time performance of visual tracking on PlayStation®3 about 7 fps with a camera of maximum 15 fps.

scholarly journals Implementation of Hybrid Model of Particle Filter and Kalman Filter based Real-Time Tracking for handling Occlusion on Beagleboard-xM

2014 ◽  
Vol 95 (7) ◽  
pp. 31-37 ◽  
Author(s):  
Jharna Majumdar ◽  
Parashar Dhakal ◽  
Nabin Sharma Rijal ◽  
Amar Mani Aryal ◽  
Nilesh Kumar Mishra
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Real Time ◽  
Hybrid Model ◽  
Real Time Tracking

REAL-TIME FLOOD FORECASTING USING A PARTICLE FILTER COMBINED WITH THE RRI MODEL AND A RIVERBED EVOLUTION MODEL

2020 ◽  
Vol 76 (2) ◽  
pp. I_859-I_864
Author(s):  
Yosuke NAKAMURA ◽  
Shinji EGASHIRA ◽  
Koji IKEUCHI ◽  
Daiki KAKINUMA
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Flood Forecasting ◽  
Evolution Model

CRAMPON: a model- and observation-based near real-time platform for glacier mass balance monitoring in Switzerland

2021 ◽  
Author(s):  
Johannes Marian Landmann ◽  
Matthias Huss ◽  
Hans Rudolf Künsch ◽  
Christophe Ogier ◽  
Lea Geibel ◽  
...  
Keyword(s):  
Particle Filter ◽  
Mass Balance ◽  
Real Time ◽  
Resource Planning ◽  
Glacier Mass Balance ◽  
Sequential Data ◽  
Mass Balances ◽  
Individual Parameter ◽  
High Interest ◽  
The Past

<p>As glaciers shrink, high interest in their near real-time mass balance arises. This is mainly for two reasons: first, there are concerns about water availability and short-term water resource planning, and second, glaciers are one of the most prominent indicators of climate change, resulting in a high interest of the broader public.</p><p>To satisfy both interests regarding information on near real-time mass balance, we are running the project CRAMPON – “Cryospheric Monitoring and Prediction Online”. Within this project, we set up an operational assimilation platform where it is possible to query daily mass balance estimates in near real-time, i.e. updated with a lag of max. 24 hours. During the operational alpha phase, we increase the amount of modelled glaciers and assimilated observations steadily. We start with about 15 glaciers from the Glacier Monitoring Switzerland (GLAMOS) program, for which time series of seasonal mass balances from the glaciological method are available. After that, we expand our set of modelled glaciers to about 50 glaciers that have frequent geodetic mass balances in the past, and finally to all glaciers in Switzerland. The assimilated observations reach from the operational GLAMOS seasonal mass balance observations via daily point mass balances from nine in situ cameras providing instantaneous ablation rates to satellite-derived albedo and snow distribution on the glacier.<br>As basis for the platform, we run an ensemble of three temperature index and one simplified energy balance melt models. This ensemble takes gridded temperature, precipitation and radiation as input and aims at quantifying uncertainties of the produced daily mass balances. To determine uncertainties in the model prediction of a current mass budget year correctly, we run the models with parameter distributions we have fitted on individual parameter sets calibrated in the past. Since a purely model-based prediction can reveal high uncertainties though, we choose a sequential data assimilation approach in the form of a Particle Filter to constrain this uncertainty with observations, whenever available. We have customized the standard Particle Filter to (1) use a resampling method that is able to keep models in the ensemble despite a temporary bad performance, and (2) allow parameter and model probability evolution over time.</p><p>In this contribution, we focus on giving a holistic overview over the already existing platform features and discuss the future developments. We plan to make the calculated mass balances publicly available in summer 2021, and to extend this platform to the global scale at a later stage.</p>

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