Tehnički vjesnik, Vol. 24 No. 3, 2017.
Izvorni znanstveni članak
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
Sažetak
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.
Ključne riječi
clustering; evolutionary computation; Locally Linear Neuro-Fuzzy model; object tracking; swarm optimization
Hrčak ID:
183041
URI
Datum izdavanja:
15.6.2017.
Posjeta: 1.313 *