Recommendations for evaluating the performance of background subtraction algorithms for surveillance systems

Sanches, Silvio Ricardo Rodrigues; Sementille, Antônio Carlos; Aguilar, Ivan Abdo; Freire, Valdinei

doi:10.1007/s11042-020-09838-x

Cited by 4 publications

(2 citation statements)

References 142 publications

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“…In order to evaluate the efficiency of the proposed algorithm [35], we use a publicly available change detection dataset 2012 (CDnet 2012) [36] and 2014 dataset (CDnet 2014) [37]. As one of the most challenging detection benchmark dataset, the CDnet 2014 dataset is a second version of the CDnet 2012 dataset.…”

Section: Evaluation Datasetsmentioning

confidence: 99%

Efficient background subtraction method based on fast independent component analysis in video-surveillance

Abderrahmane,

Boumehed,

Alshaqaqi

et al. 2023

IJEECS

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Modern video surveillance has now become an active area of research with a large set of requirements and various applications. In order to detect moving objects in video surveillance scenes, background subtraction techniques are the most used. In this paper, we developed and tested an efficient background subtraction technique in video surveillance based on the fast-independent component analysis (fast-ICA) method. The proposed technique initiated, first, on the use of a developed fast-ICA algorithm in order to estimate the de-mixing matrix and the denoising matrix parameters. Second, the estimated foreground can simply model by multiplying the data matrix with the de-mixing matrix. After that, the data matrix is multiplied by the denoising matrix for removing the noise. In addition, we propose a pre-processing and post-processing operations to effectively segment the true foreground objects and improve our results. The proposed method is evaluated on the publicly available change detection datasets CDnet 2012 and CDnet 2014 using performance parameters such as recall, precision and experimental results show that our algorithm can detect effectively and accurately the moving objects in several background and foreground conditions compared to other methods in literature with real-time frame rate.

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Section: Evaluation Datasetsmentioning

confidence: 99%

Efficient background subtraction method based on fast independent component analysis in video-surveillance

Abderrahmane,

Boumehed,

Alshaqaqi

et al. 2023

IJEECS

View full text Add to dashboard Cite

show abstract

“…The adaptive adjustment of the variation probability Pm uses the variation of two numbers generated by a random function, such as the variation of the time and teacher code in the schedule of a class. The performance of the genetic algorithm is affected by crossover and variation, and the magnitude of the adaptive variation probability is not a fixed value but varies with the crossover probability [20]. These two operations, adaptive crossover, and variation are coordinated with each other to ensure the global search ability of the genetic algorithm to obtain the global optimal solution.…”

Section: Figure 1 Improved Adaptive Genetic Algorithm Frameworkmentioning

confidence: 99%

An Adaptive Genetic Algorithm-based Background Elimination Model for English Text

Xiaohui¹

2021

Preprint

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In this paper, an adaptive genetic algorithm is used to conduct an in-depth study and analysis of English text background elimination, and a corresponding model is designed. The curve results after the initial character editorialization are curved and transformed, and the adaptive genetic algorithm is used for the transformation to solve the influence of multiple inflection points of curve images on feature extraction. Then, using the minimum deviation method, the error values of the input characters and the sample set in the spatial coordinate system are calculated, and the deviation values of the angle and the straight line are used to match the characters with the smallest deviation value to match the highest degree. A genetic algorithm is introduced to iterate the feature sets of angles and line segments, and the optimal features are finally derived in the process of cross evolution of generations to improve the recognition accuracy. And the character library is used as input items for average grouping for experiments, and the obtained feature sets are put into the position matrix and compared with the samples in the database one by one. It is found that the improved stroke-structure feature extraction algorithm based on a genetic algorithm can improve the recognition accuracy and better accomplish the recognition task with better results compared to others. Finally, by analyzing the limitations and characteristics of traditional particle swarm optimization algorithm and differential evolution algorithm, and giving full play to the advantages and applicability of different algorithms, a new differential evolution particle swarm algorithm with better performance and more stable performance is proposed. The algorithm is based on the PSO algorithm, and when the population update of the PSO algorithm is stagnant and the search space is limited, the crossover and mutation operations of the DE algorithm are used to perturb the population, increase the diversity of the population, and improve the global optimization ability of the algorithm. The algorithm is tested on a common dataset for text mining to verify the effectiveness and feasibility of the algorithm.

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An adaptive genetic algorithm-based background elimination model for English text

Xiaohui

2022

Soft Comput

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Recommendations for evaluating the performance of background subtraction algorithms for surveillance systems

Cited by 4 publications

References 142 publications

Efficient background subtraction method based on fast independent component analysis in video-surveillance

Efficient background subtraction method based on fast independent component analysis in video-surveillance

An Adaptive Genetic Algorithm-based Background Elimination Model for English Text

An adaptive genetic algorithm-based background elimination model for English text

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