@inproceedings{f283c0f6395148778b97e21869da2d76,
title = "An object tracking algorithm based on adaptive particle filtering and deep correlation multi-model",
abstract = "An object tracking algorithm based on adaptive particle filtering and deep correlation multi-model is proposed to solve the problem of large numbers of particles, as well as the defects in the generation of object model in the conventional correlation particle filter. The proposed algorithm generates multi-object model by applying different adjustment rates to each high likelihood particle, and updates and predicts the particles adaptively according to the weight of correlation response graph and particle position. The proposed algorithm can adaptively adjust the number of particles according to the complexity of the tracking scene to obtain more useful particles, solve the problem of the conventional algorithm in model generation, and improve the tracking performance. The experimental results compared with some existing tracking algorithms on OTB100 datasets show that the proposed algorithm can track the object more accurately and stably under the influence of various challenging factors.",
keywords = "Object tracking, adaptive particle filter, convolutional neural network, object model",
author = "Keke Duan and Yue Yu",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE; 3rd International Conference on Electronics and Communication; Network and Computer Technology, ECNCT 2021 ; Conference date: 03-12-2021 Through 05-12-2021",
year = "2022",
doi = "10.1117/12.2628642",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "El-Zoghdy, {Said Fathy} and Mohiddin, {Md Khaja}",
booktitle = "Third International Conference on Electronics and Communication; Network and Computer Technology, ECNCT 2021",
address = "United States",
}