The Particle Filter Algorithm Research for Target Tracking Based on Information Fusion

Li, Juan; Hao, Hui; Wang, Mao Li

doi:10.4028/www.scientific.net/amr.628.440

Cited by 3 publications

(1 citation statement)

References 4 publications

(4 reference statements)

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“…Pan Lian [17] proposed a pedestrian tracking system on ARM. Li Yanbin [18] modified Particle Filter on DSP. Deng Minxi [19] built a real-time target tracking system on DSP.…”

Section: Introductionmentioning

confidence: 99%

Parallel Particle Filter Algorithm and Its FPGA Implementation

Lu¹,

Wang²,

Du³

et al. 2014

Proceedings of the 2014 International Conference on Computer, Communications and Information Technology

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-Visual target tracking is the key problem in intelligent video processing. Particle Filter is classic and effective in visual target tracking algorithms, but it needs to analyze a large amount of probability statistic, leading to high algorithm complexity and low calculation efficiency. FPGA provides a competitive alternative for hardware acceleration to these applications. In this paper, we modify Particle Filter algorithm and propose a FPGAbased hardware accelerating architecture. Experiments show the embedded architecture is robust and shows a great real-time performance.

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“…Pan Lian [17] proposed a pedestrian tracking system on ARM. Li Yanbin [18] modified Particle Filter on DSP. Deng Minxi [19] built a real-time target tracking system on DSP.…”

Section: Introductionmentioning

confidence: 99%

Parallel Particle Filter Algorithm and Its FPGA Implementation

Lu¹,

Wang²,

Du³

et al. 2014

Proceedings of the 2014 International Conference on Computer, Communications and Information Technology

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show abstract

Features of Phase Transformations in Martensitic Class Steel for Oil Grade High-Strength Corrosion-Resistant Pipes

et al. 2022

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Austenite Formation from Martensite in a 13Cr6Ni2Mo Supermartensitic Stainless Steel

Bojack

Zhao

Morris

et al. 2016

Metall Mater Trans A

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The influence of austenitization treatment of a 13Cr6Ni2Mo supermartensitic stainless steel (X2CrNiMoV13-5-2) on austenite formation during reheating and on the fraction of austenite retained after tempering treatment is measured and analyzed. The results show the formation of austenite in two stages. This is probably due to inhomogeneous distribution of the austenite-stabilizing elements Ni and Mn, resulting from their slow diffusion from martensite into austenite and carbide and nitride dissolution during the second, higher temperature, stage. A better homogenization of the material causes an increase in the transformation temperatures for the martensite-to-austenite transformation and a lower retained austenite fraction with less variability after tempering. Furthermore, the martensite-to-austenite transformation was found to be incomplete at the target temperature of 1223 K (950°C), which is influenced by the previous austenitization treatment and the heating rate. The activation energy for martensite-to-austenite transformation was determined by a modified Kissinger equation to be approximately 400 and 500 kJ/mol for the first and the second stages of transformation, respectively. Both values are much higher than the activation energy found during isothermal treatment in a previous study and are believed to be effective activation energies comprising the activation energies of both mechanisms involved, i.e., nucleation and growth.

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The Particle Filter Algorithm Research for Target Tracking Based on Information Fusion

Cited by 3 publications

References 4 publications

Parallel Particle Filter Algorithm and Its FPGA Implementation

Parallel Particle Filter Algorithm and Its FPGA Implementation

Features of Phase Transformations in Martensitic Class Steel for Oil Grade High-Strength Corrosion-Resistant Pipes

Austenite Formation from Martensite in a 13Cr6Ni2Mo Supermartensitic Stainless Steel

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