Visual tracking method based on cuckoo search algorithm

Gao, Mingliang; Yin, Lu; Zou, Guofeng; Li, Haitao; Liu, Wei

doi:10.1117/1.oe.54.7.073105

Cited by 22 publications

(5 citation statements)

References 23 publications

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“…Gao et al [ 21 ] proposed a tracker based on the Cuckoo Search (CS) [ 6 ] algorithm. The CS algorithm mimics the predatory behavior of the cuckoo bird in relation to the laying of eggs during the nesting period.…”

Section: Related Workmentioning

confidence: 99%

“…A classic tracker in this category is the Mean Shift (MS) [ 17 ] and other models in this category can be found in [ 18 ]. Therefore, there is a class of trackers whose models are based on optimization algorithms since the likely position of the target in a frame is indicated by the position of maximum similarity between the templates and the candidate target [ 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

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A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking

Castro

Salles

Ciarelli

2021

Sensors

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This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search. A robust version to outliers of the double exponential smoothing (DES) model is used to predict the target position in the frame delimiting the solution space in a more promising region for target tracking. To assess the quality of the proposed approach, an appropriate tracker for a discrete solution space was implemented using the meta-heuristic Shuffled Frog Leaping Algorithm (SFLA) adapted to dynamic optimization problems, named the Dynamic Shuffled Frog Leaping Algorithm (DSFLA). The DSFLA was compared with other classic and current trackers whose algorithms are based on swarm intelligence. The trackers were compared in terms of the average processing time per frame and the area under curve of the success rate per Pascal metric. For the experiment, we used a random sample of videos obtained from the public Hanyang visual tracker benchmark. The experimental results suggest that the DSFLA has an efficient processing time and higher quality of tracking compared with the other competing trackers analyzed in this work. The success rate of the DSFLA tracker is about 7.2 to 76.6% higher on average when comparing the success rate of its competitors. The average processing time per frame is about at least 10% faster than competing trackers, except one that was about 26% faster than the DSFLA tracker. The results also show that the predictions of the robust DES model are quite accurate.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking

Castro

Salles

Ciarelli

2021

Sensors

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“…In this context, Maria et al [15] introduce GA in hyperspectral images analysis, where they introduce a GA based on mutual information and normalized mutual information as fitness functions based on mutual information to achieve this bands selection. While Gao et al [16] recommend CS algorithm to solve tracking problem, the algorithm has two parameters the number of nests and the probability discovering, it initializes the nest then it replaces them by Levy Flight random model while calculating the fitness of each nest. The best observation represents the target for the current frame.…”

Section: Introductionmentioning

confidence: 99%

Self scale estimation of the tracking window merged with adaptive particle filter tracker

Azdoud

Aouatif²,

Bouchra³

et al. 2023

IJECE

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Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A Reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker.

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“…The swarm optimization algorithms as a kind of search strategy combine global exploration with local exploit to achieve global optimization, and have received extent attention. Some researchers have proposed trackers based on swarm optimization algorithm [14][15][16][17][18] and achieved good results.…”

Section: Introductionmentioning

confidence: 99%

A new visual tracking approach based on salp swarm algorithm for abrupt motion tracking

2020

KSII TIIS

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Salp Swarm Algorithm (SSA) is a new nature-inspired swarm optimization algorithm that mimics the swarming behavior of salps navigating and foraging in the oceans. SSA has been proved to enable to avoid local optima and enhance convergence speed benefiting from the adaptive nonlinear mechanism and salp chains. In this paper, visual tracking is considered to be a process of locating the optimal position through the interaction between leaders and followers in successive images. A novel SSA-based tracking framework is proposed and the analysis and adjustment of parameters are discussed experimentally. Besides, the qualitative analysis and quantitative analysis are performed to demonstrate the tracking effect of our proposed approach by comparing with ten classical tracking algorithms. Extensive comparative experimental results show that our algorithm has good performance in visual tracking, especially for abrupt motion tracking.

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Visual tracking method based on cuckoo search algorithm

Cited by 22 publications

References 23 publications

A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking

A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking

Self scale estimation of the tracking window merged with adaptive particle filter tracker

A new visual tracking approach based on salp swarm algorithm for abrupt motion tracking

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