3,375 research outputs found
Object Detection using Particle Swarm Optimisation and Kalman Filter to Track Partially occluded Targets
Motion estimation, object detection, and tracking have been actively pursued by researchers in the field of real time video processing. In the present work, a new algorithm is proposed to automatically detect objects using revised local binary pattern (m-LBP) for object detection. The detected object was tracked and its location estimated using the Kalman filter, whose state covariance matrix was tuned using particle swarm optimisation (PSO). PSO, being a nature inspired algorithm, is a well proven optimization technique. This algorithm was applied to important real-world problems of partially-occluded objects in infrared videos. Algorithm validation was performed by realizing a thermal imager, and this novel algorithm was implemented in it to demonstrate that the proposed algorithm is more efficient and produces better results in motion estimation for partially-occluded objects. It is also shown that track convergence is 56% faster in the PSO-Kalman algorithm than tracking with Kalman-only filter
BFO vs. BSO for video object tracking using particle filter (PF)
In this paper, we introduced a new algorithm for Video tracking, which is the process of locating a moving object (or multiple objects) over time using a camera. A new particle filter based on bacteria foraging optimization (PF-BFO) is introduced in field of video object tracking. This paper reviews particle filter and using it for tracking. Particle Swarm Optimization (PSO) is also described. Moreover, using the combination of PSO with PF (PF-PSO) in video object tracking is reviewed. Bacterial Foraging Optimization (BFO) is a novel heuristic algorithm inspired from forging behavior of E. coli. After analysis of optimization mechanism, a series of measures are taken to improve the classic BFO by using Particle filter. The PSO is a meta-heuristic which is also inspired from insects' life as ACO. Even both methods use a population of entities. The comparison between PF-BFO and PF-PSO for video object tracking is presented in this work. The results show that PF is strong tool in tracking field. On the other hand, PF-BFO method presents outstanding performance versus PF-PSO
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Iterated filtering methods for Markov process epidemic models
Dynamic epidemic models have proven valuable for public health decision
makers as they provide useful insights into the understanding and prevention of
infectious diseases. However, inference for these types of models can be
difficult because the disease spread is typically only partially observed e.g.
in form of reported incidences in given time periods. This chapter discusses
how to perform likelihood-based inference for partially observed Markov
epidemic models when it is relatively easy to generate samples from the Markov
transmission model while the likelihood function is intractable. The first part
of the chapter reviews the theoretical background of inference for partially
observed Markov processes (POMP) via iterated filtering. In the second part of
the chapter the performance of the method and associated practical difficulties
are illustrated on two examples. In the first example a simulated outbreak data
set consisting of the number of newly reported cases aggregated by week is
fitted to a POMP where the underlying disease transmission model is assumed to
be a simple Markovian SIR model. The second example illustrates possible model
extensions such as seasonal forcing and over-dispersion in both, the
transmission and observation model, which can be used, e.g., when analysing
routinely collected rotavirus surveillance data. Both examples are implemented
using the R-package pomp (King et al., 2016) and the code is made available
online.Comment: This manuscript is a preprint of a chapter to appear in the Handbook
of Infectious Disease Data Analysis, Held, L., Hens, N., O'Neill, P.D. and
Wallinga, J. (Eds.). Chapman \& Hall/CRC, 2018. Please use the book for
possible citations. Corrected typo in the references and modified second
exampl
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