Spider monkey optimisation assisted particle filter for robust object tracking

Rohilla, Rajesh; Sikri, Vanshaj; Kapoor, Rajiv

doi:10.1049/iet-cvi.2016.0201

Cited by 20 publications

(5 citation statements)

References 40 publications

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“…Step 5: Return the Global Leader as the final solution diabetes classifications [41], designing optimal fuzzy rule-base for a Tagaki-Sugeno-Kang (TSK) fuzzy control system [42], improving quality and diversity of particles and distributing them in particle filters to provide a robust object tracking framework [43], CDMA multiuser detection [44], optical power flow, pattern synthesis of sparse linear array and antenna arrays [45], numerical classification [46], optimizing frequency in microgrid [47], economic dispatch problem [48], optimizing models of multi-reservoir system [49], and energy efficient clustering for WSNs [50].…”

Section: Step 4: Goto Step 3 If Termination Condition Is Not Metmentioning

confidence: 99%

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Akhand

Ayon

Shahriyar

et al. 2020

Applied Soft Computing

159

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Meta-heuristic algorithms inspired by biological species have become very popular in recent years. Collective intelligence of various social insects such as ants, bees, wasps, termites, birds, fish, has been investigated to develop a number of meta-heuristic algorithms in the general domain of swarm intelligence (SI). The developed SI algorithms are found effective in solving different optimization tasks. Traveling Salesman Problem (TSP) is the combinatorial optimization problem where a salesman starting from a home city travels all the other cities and returns to home city in the shortest possible path. TSP is a popular problem due to the fact that the instances of TSP can be applied to solve real-world problems, implication of which turns TSP into a standard test bench for performance evaluation of new algorithms. Spider Monkey Optimization (SMO) is a recent addition to SI algorithms based on the social behavior of spider monkeys. SMO implicitly adopts grouping and regrouping for the interactions to improve solutions; such multi-population approach is the motivation of this study to develop an effective method for TSP. This paper presents an effective variant of SMO to solve TSP called discrete SMO (DSMO). In DSMO, every spider monkey represents a TSP solution where Swap Sequence (SS) and Swap Operator (SO) based operations are employed, which enables interaction among monkeys in obtaining the optimal TSP solution. The SOs are generated using the experience of a specific spider monkey as well as the experience of other members (local leader, global leader, or a randomly selected spider monkey) of the group. The performance and effectiveness of the proposed method have been verified on a large set of TSP instances and the outcomes are compared to other well-known methods. Experimental results demonstrate the effectiveness of the proposed DSMO for solving TSP.

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Section: Step 4: Goto Step 3 If Termination Condition Is Not Metmentioning

confidence: 99%

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Akhand

Ayon

Shahriyar

et al. 2020

Applied Soft Computing

159

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“…However, the above work is tested in the case of a nonlinear function, without further verification of the video sequences, so there are shortcomings. To resolve the problem of sample impoverishment of the particle filter in target tracking, an optimized auxiliary particle filter algorithm based on spider monkey was proposed in the literature [21]. Nenavath et al [22] presented guidance regarding particle position by using a sine cosine optimization algorithm.…”

Section: Improved Particle Filter Based On Sample Impoverishmentmentioning

confidence: 99%

Particle Filter Based on Harris Hawks Optimization Algorithm for Underwater Visual Tracking

Yang

Yao

Yang

2023

JMSE

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Due to the complexity of the underwater environment, tracking underwater targets via traditional particle filters is a challenging task. To resolve the problem that the tracking accuracy of a traditional particle filter is low due to the sample impoverishment caused by resampling, in this paper, a new tracking algorithm using Harris-hawks-optimized particle filters (HHOPF) is proposed. At the same time, the problem of particle filter underwater target feature construction and underwater target scale transformation is addressed, the corrected background-weighted histogram method is introduced into underwater target feature recognition, and the scale filter is combined to realize target scaling transformation during tracking. In addition, to enhance the computational speed of underwater target tracking, this paper constructs a nonlinear escape energy using the Harris hawks algorithm in order to balance the exploration and exploitation processes. Based on the proposed HHOPF tracker, we performed detection and evaluation using the Underwater Object Tracking (UOT100) vision database. The proposed method is compared with evolution-based tracking algorithms and particle filters, as well as with recent tracker-based correlation filters and some other state-of-the-art tracking methods. By comparing the results of tracking using the test data sets, it is determined that the presented algorithm improves the overlap accuracy and tracking accuracy by 11% compared with other algorithms. The experiments demonstrate that the presented HHOPF visual tracking provides better tracking results.

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“…SI has been used for object tracking in a few works [25,26], but as far as we know, the parallelized SI algorithms have been mostly tested with benchmarking functions, and less frequently used for object tracking [27]. This work uses the unique combination of: NCC as the tracker function, the HSA for heuristic search, and a GPU for hardware acceleration.…”

Section: Introductionmentioning

confidence: 99%

Parallelization of the Honeybee Search Algorithm for Object Tracking

Perez-Cham

Puente

Soubervielle‐Montalvo

et al. 2020

Applied Sciences

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Object tracking refers to the relocation of specific objects in consecutive frames of a video sequence. Presently, this visual task is still considered an open research issue, and the computer science community attempted solutions from the standpoint of methodologies, algorithms, criteria, benchmarks, and so on. This article introduces a GPU-parallelized swarm algorithm, called the Honeybee Search Algorithm (HSA), which is a hybrid algorithm combining swarm intelligence and evolutionary algorithm principles, and was previously designed for three-dimensional reconstruction. This heuristic inspired by the search for food of honeybees, and here adapted to the problem of object tracking using GPU parallel computing, is extended from the original proposal of HSA towards video processing. In this work, the normalized cross-correlation (NCC) criteria is used as the fitness function. Experiments using 314 video sequences of the ALOV benchmark provides evidence about the quality regarding tracking accuracy and processing time. Also, according to these experiments, the proposed methodology is robust to high levels of Gaussian noise added to the image frames, and this confirms that the accuracy of the original NCC is preserved with the advantage of acceleration, offering the possibility of accelerating latest trackers using this methodology.

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Spider monkey optimisation assisted particle filter for robust object tracking

Cited by 20 publications

References 40 publications

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Particle Filter Based on Harris Hawks Optimization Algorithm for Underwater Visual Tracking

Parallelization of the Honeybee Search Algorithm for Object Tracking

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