#particle filter

[andys-tradies]

SELFIE 2019

[andys-tradies]

SELFIE 2019

[ijtsrd]

Advanced Technique for Real Time Detection and Recognition of Object using Resampling and BPF

By Sharda R. Chaudhary | Nutan Y. Suple | Punam Lambat "Advanced Technique for Real Time Detection and Recognition of Object using Resampling and BPF"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017,

URL: http://www.ijtsrd.com/papers/ijtsrd7123.pdf

http://www.ijtsrd.com/computer-science/real-time-computing/7123/advanced-technique-for-real-time-detection-and--recognition-of-object-using-resampling-and-bpf/sharda-r-chaudhary

international journal of management, call for paper technology, ugc listed journals, indexed journal

This paper will explain the bayes filtering system which will explain why should to prefer the particle filters than any other method required for detection of an object in real time videos. It also gives the basic information about the kalman filters, the disadvantages of it and how they are removed in the particle filters. Resampling method related to the pdf of object density for deciding the weight of an object is also included in the paper. The basic steps of particle filters are also well explained. This paper is related to the belief ideas of presence and absence of an object in a particular frame so the false and true conditions can be checked easily than any other method.

[nicolaberniniblog]

Introduction to Particle Filter

Introduction to Recursive Bayesian Filters 

Let's introduce the general formula of Recursive Bayesian Filters for Hidden Markov Processes 

General Recursive Bayesian Filter 

The elements of the formula are the following ones 

$ P(x_{t} | y_{1:t}) $ State PDF given a set of Observations in different times 

$ \alpha $ Normalization Term 

$ P(y_{t} | x_{t}) $ Observation PDF given a certain State, also called Likelihood 

$ P(x_{t} | x_{t-1}) $ Process Dynamic, it is to say the PDF of the State at a certain time, given the State at the previous instant 

$ P(x_{t-1} | y_{t-1}) $ State PDF calculated at the previous iteration (it explains why this is called a Recursive Filter) 

Numerical Estimation 

In general the integral in the abovementioned formula has no closed form solution (except in very specific situations) so a numerical approximation is needed 

The Particle Filter is a Monte Carlo Method to compute a numerical approximation for this integral relying on the concept of particle 

The term Particle designate a Possible System State. 

In order to be able to adopt this solution, it is necessary to know 

the System Dynamic 

the System Likelihood 

The Particle Filter strategy so is simply as follows 

at the beginning of the iteration cycle (recursive filter) a certain amount of Particles is generated, for example by sampling a certain PDF chosen in some way 

at every iteration, the particles are evolved according to the System Dynamic thus to be able to numerically estimate the integral 

Particle Filter is a Monte Carlo Method to numerically estimate the Integral 

[danvil-blog]

Event-based Particle Filtering for Robot Self-Localization

For those more interested in science, a small remark about my recent scientific research. I published an interesting, new method for robot self-localization using a low-profile event-based sensor. See here and here for more information about the sensor.

The main challenge was to adapt well known methods in computer vision to a new kind of hardware. Instead of full images at a fixed frame rate our method works with a stream of individual pixel events. This has the advantage that much less computation power, almost no memory and much less electrical power is required - making our method especially suitable for embedded applications.

Next week I will fly to China and present the paper a the Conference on Robotics and Biomimetics - hooray! This will be an exciting trip and I am really curious what China is alike.