Original scientific paper

https://doi.org/10.17559/TV-20151005211208

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Fatemeh Saadian ; Sama Technical and Vocational Training College, Islamic Azad University, Nour Branch, Nour, Iran

Abstract

In this paper a new method based on evolutionary clustering and locally linear neuro-fuzzy (LLNF) models is proposed for the problem of object tracking in videos. This approach utilizes clustering on color feature space to obtain a model of object which is given at the initial frame. To achieve the optimal clustering, evolutionary optimization methods are used. Based on the results of clustering, parameters of LLNF model is determined so it can be used as an identifier of object during the real time video streaming. To track the object, a swarm of weighted evolving linear models are used to estimate the location and size of the object at next frame based on its current and previous states. The performance of the proposed method is evaluated on a benchmark data set and compared to other methods performed on the same data set. The results show that the accuracy of the proposed method is superior to previous methods.

Keywords

clustering; evolutionary computation; Locally Linear Neuro-Fuzzy model; object tracking; swarm optimization

Hrčak ID:

183041

URI

https://hrcak.srce.hr/183041

Publication date:

15.6.2017.

Article data in other languages: croatian

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