An efficient algorithm for tracking and counting pedestrians based on feature points in video surveillance applications

Kanagamalliga, S.; Vasuki, S.

doi:10.3233/jifs-172257

Cited by 10 publications

(1 citation statement)

References 20 publications

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“…In the era of big data, the size of large data usually dynamically increases, including the current changing and interconnected datasets [26]. In the future, more research should be carried out allowing the DSF-FS algorithm to classify above-mentioned datasets.…”

Section: Conclusion and Future Directionmentioning

confidence: 99%

SVM-Based Feature Selection for Differential Space Fusion and Its Application to Diabetic Fundus Image Classification

Ding

2019

IEEE Access

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Data mining is one of the most important applications of machine learning. In machine learning algorithms, the fusion kernel principle component analysis (KPCA) and support vector machine (SVM) algorithm is used in complex data classification. To solve the problem that the fusion KPCA and SVM algorithm does not have promising classification performance, the SVM based on a feature selection algorithm for differential space fusion (DSF-FS) is proposed. First, the original data is processed to obtain differential space data by principle component analysis (PCA), and the KPCA algorithm is performed respectively on the original data and differential space data to get the differential space fusion features. Second, the ReliefF algorithm is used to get the weight of features, and the optimal feature combination is selected by a preliminary classification evaluation metric. Third, the SVM algorithm is used to classify the dimensionality reduction data. Finally, some experimental results on the five UCI datasets show that the proposed DSF-FS algorithm can not only improve the classification accuracy, but it can also reduce the computational complexity of the classification process. Moreover, the DSF-FS algorithm can be successfully applied in diabetic fundus image classification, and the encouraging results further demonstrate its strong feasibility and applicability.INDEX TERMS Kernel principle component analysis, SVM, differential space data, relieff algorithm, diabetic fundus image classification.

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Section: Conclusion and Future Directionmentioning

confidence: 99%

SVM-Based Feature Selection for Differential Space Fusion and Its Application to Diabetic Fundus Image Classification

Ding

2019

IEEE Access

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Interactive teaching learning based optimization technique for multiple object tracking

Dash

Mishra

Senapati

et al. 2020

Multimed Tools Appl

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Advancements in Real-Time Face Recognition Algorithms for Enhanced Smart Video Surveillance

Krishna

et al. 2023

Procedia Computer Science

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An efficient algorithm for tracking and counting pedestrians based on feature points in video surveillance applications

Cited by 10 publications

References 20 publications

SVM-Based Feature Selection for Differential Space Fusion and Its Application to Diabetic Fundus Image Classification

SVM-Based Feature Selection for Differential Space Fusion and Its Application to Diabetic Fundus Image Classification

Interactive teaching learning based optimization technique for multiple object tracking

Advancements in Real-Time Face Recognition Algorithms for Enhanced Smart Video Surveillance

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