Volume 14, Issue 4 (Journal of Control, V.14, N.4 Winter 2021)
JoC 2021, 14(4): 67-79 |
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Rohani M, farsi H, Zahiri mamghani S H. Moving object tracking in video by using fuzzy particle swarm optimization algorithm. JoC 2021; 14 (4) :67-79
URL: http://joc.kntu.ac.ir/article-1-672-en.html
URL: http://joc.kntu.ac.ir/article-1-672-en.html
1- University of Birjand
Abstract: (6391 Views)
Nowadays, one of the most fundamental processes for realization video of contents is the object tracking, in which the process of location the moving object is performed in each video frame. In tracking process, the target must be described by a feature. In this paper, for the purpose of describing the target and removing the appearance sensitivity, the weighted color histogram is used as a target feature in order to reduce the effect of edge pixels on the target feature. This reduces the sensitivity of the algorithm to change deformation, scale variation and rotation, as well as the occlusion on the description of target feature. In the proposed method, particle swarm optimization algorithm has been used for search process. Maximization of the similarity function and calculating the minimum Bhattacharyya distance are used to determine target location. The fuzzy control parameters are used for the particle swarm optimization algorithm, which provides a novel method, which can regulate each control parameter and update according to the different states of each particle in each generation. The improved particle swarm algorithm is evaluated with 11 benchmark functions. The obtained results by improved algorithm show that appropriate convergence in a low number of iterations. The proposed method compared to state-of-the-art methods provides high performance in the success and precision rate on the OTB50 dataset.
Keywords: Object tracking, Improved particle swarm optimization algorithm, Weighted color histogram feature.
Type of Article: Research paper |
Subject:
Special
Received: 2019/05/26 | Accepted: 2020/02/5 | ePublished ahead of print: 2020/10/5 | Published: 2021/02/19
Received: 2019/05/26 | Accepted: 2020/02/5 | ePublished ahead of print: 2020/10/5 | Published: 2021/02/19
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