Abstract
In this study, a target tracking algorithm based on the flower pollination algorithm (FPA) is proposed. This method solves the problem of robust visual target tracking in different complex tracking scenes with the good global and local optimisation ability of the FPA. Meanwhile, with the aim of solving the problem of invalid background feature interference and the loss of effective features caused by the fixed scale of tracking frame in traditional tracking methods, a scale adaptive adjustment model of tracking frame is proposed. Considering that the FPA has good global and local optimization ability at simultaneously, the position update equation of the FPA is introduced as the main optimization method of target tracking. In addition, considering that the traditional FPA is similar to classical swarm intelligence algorithm (such as the particle swarm optimization algorithm), it also faces the problems of a high probability of falling into local extrema, a low efficiency of late convergence speed and a high probability of early maturity. Therefore, this work proposes the GTFPA, an advanced FPA based on the gravitational search algorithm (GSA) and mutation mechanism via a trigonometric function. We qualitatively, quantitatively and statistically compare the proposed method with other classical general tracking methods through two datasets, OTB2015 and VOT2018, which contain hundreds of video sequences and more than ten tracking scenes and can effectively test the success rate, accuracy and stability of the trackers. The results of a large number of tracking experiments in a variety of complex tracking scenarios prove that the proposed GTFPA tracker performs well with regards to efficiency, accuracy and robustness.
Highlights
Visual target tracking is an important research hotspot in the field of machine vision and has been applied to many cutting-edge technologies, such as vehicle assisted driving, competitive photography, virtual reality and crime prediction [1,2,3]
Related Works Ref. [7] proposed the efficient convolution operators (ECO) for tracking, which simplified the parameters of the discriminative correlation filter (DCF) by introducing a factorized convolution operator
We evaluate the robustness and stability of these trackers by carrying out statistical comparison between our tracker with eight state-of-the-art tracking methods in VOT2018 including DeepSTRCF [16], SiamVGG [17], ECO, GTFPA, MCCT [18], continuous convolution operators (C-COT), Staple, SRDCF [44] and discriminative scale space tracker (DSST)
Summary
Visual target tracking is an important research hotspot in the field of machine vision and has been applied to many cutting-edge technologies, such as vehicle assisted driving, competitive photography, virtual reality and crime prediction [1,2,3]. An improved FPA algorithm based on the gravitational search algorithm (GCA) and mutation mechanism via a trigonometric function (GTFPA) is proposed This new method helps solve the common problems of the standard FPA, including the high probability of falling into. A conversion probability adjustment mechanism based on an exponential function is proposed, so that the conversion probability decreases dynamically with the iteration of the algorithm This mechanism ensures that the FPA tracker can fully explore the search space in the early stages and increase the accuracy of the global optimal value, and ensures that the algorithm can accelerate the convergence speed in the later stage of iteration. Through the comparison of qualitative, quantitative and statistical experiments on OTB2015 and VOT2018, it can be seen that the newly proposed GTFPA tracking algorithm performs well in efficiency, accuracy and robustness, and the visual tracking results under eight typical tracking scenarios show that the tracking frame of GTFPA algorithm can always closely fit the tracked object and has good tracking accuracy and stability
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