Tracking and Analysis of Pedestrian’s Behavior in Public Places

Pervaiz, Mahwish; Alsufyani, Abdulmajeed; Jalal, Ahmad; Alsuhibany, Suliman A.; Park, Jeong-Min

doi:10.32604/cmc.2023.029629

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…6) Fighting: Two or more people fighting with the potential to injure each other [36]. Detection of these fights has been carried out by several researchers to minimize the impact that might occur [37]- [40]. Just like the previous category, this fight behavior includes short-period abnormal behavior because just a single frame can be detected.…”

Section: A Short-period Abnormal Behaviormentioning

confidence: 99%

Unsupervised Learning Methods for Human Abnormal Behavior Detection in Surveillance Systems; A Survey

Wastupranata

2024

Preprint

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Human Abnormal Behavior Detection is needed in several important sectors, especially in the field of public safety. Video Surveillance Systems are a trigger in detecting strange human behavior. However, this abnormal behavior data is rare and requires more costs to obtain, so the unsupervised learning method is used to study normal human behavior patterns only. There have been several previous studies that have provided significant results using this technique. There have been several previous survey studies related to human abnormal behavior detection, but they did not focus on unsupervised learning methods and the scope was too broad. In this paper, I present a survey regarding human abnormal behavior detection in more depth conceptually and classify it into two large parts, such as reconstruction-based detection and generative-based detection. This paper also offers a more extensive comparison with several popular datasets that are often used in previous research. Finally, I also shared several challenges and open research issues that emerged from our survey regarding future directions for future development.

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Section: A Short-period Abnormal Behaviormentioning

confidence: 99%

Unsupervised Learning Methods for Human Abnormal Behavior Detection in Surveillance Systems; A Survey

Wastupranata

2024

Preprint

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“…XGB is a scalable tree boosting system with high efficiency and prediction accuracy consisting of many decision trees and is typically used in the field of regression. XGB uses second-order Taylor expansion to the loss function and normalization is utilized in the objective function to prevent over-fitting to outliers [20,21,[23][24][25][26][27].…”

Section: Xgb Regressormentioning

confidence: 99%

Machine learning models to predict the relationship between printing parameters and tensile strength of 3D Poly (lactic acid) scaffolds for tissue engineering applications

Ege,

Sertturk,

Acarkan

et al. 2023

Biomed. Phys. Eng. Express

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3D printing is an effective method to prepare 3D scaffolds for tissue engineering applications. However, optimization of printing conditions to obtain suitable mechanical properties for various tissue engineering applications is costly and time consuming. To address this problem, in this study, scikit-learn Python machine learning library was used to apply four machine learning-based approaches which are ordinary least squares (OLS) linear regression, random forest (RF), light gradient Boost (LGBM) and extreme gradient boosting (XGB) models to understand the relationship between 3D printing parameters and tensile strength of poly(lactic acid) (PLA). 68 combinations of process parameters for nozzle temperature, printing speed, layer height and tensile strength were used from investigated research papers. Then, datasets were divided as training (80%) and test (20%). After building the OLS linear regression, RF, LGBM and RF models, the correlation heatmap and feature importance of each printing parameter for tensile strength values were determined, respectively. Then, the tensile strength was predicted for real datasets to evaluate the performance of the models. The results demonstrate that XGB model was the most successful in predicting tensile strength among the studied models with an R2 value of 0.98 and 0.94 for train and test values, respectively. A close R2 value for the train and test also indicated that there was no overfitting of the data to the model. This study can be extended for independent variables including nozzle pressure, strut size and molecular weight of PLA and dependent variables such as elongation and elastic modulus of PLA which may be a powerful tool to predict the mechanical properties of scaffolds for tissue engineering applications.

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“…XGB is known for its efficiency and prediction accuracy, making it a common choice in regression tasks. To prevent over-fitting to outliers, XGB applies a second-order Taylor expansion to the loss function and normalization to the objective function [5][6][7]9].…”

Section: Introductionmentioning

confidence: 99%

Investigation of effect of processing parameters of 3D printed NHS/EDC crosslinked carboxy methyl cellulose/gelatin hydrogels with machine learning techniques

Ege,

Arıcı

2024

Mater. Res. Express

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This study focuses on the effects of preparing conditions of gelatin/carboxymethyl cellulose (CMC) composites on their mechanical properties of gelatin/carboxymethyl cellulose (CMC) by extreme gradient boosting (XGB) machine learning algorithm. The research involved studying the effect of weight fraction of carboxymethyl cellulose (CMC) and graphene oxide (GO) as well as the concentration of ethyl(dimethylaminopropyl)carbodiimide (EDC)/ N-hydroxysuccinimide (NHS) on modulus, % strain at break and ultimate tensile strength (UTS). It also includes a correlation heatmap, feature importance assessment, model performance evaluation, and the Shapley Additive Explanation (SHAP) technique to analyze the dataset. The relationship between independent parameters and mechanical properties reveals insights into the material's ductility, flexibility, and modulus. Feature importance demonstrates that NHS/EDC concentration has the highest impact on the mechanical properties. Increase of EDC/NHS concentration is observed to drastically elevate the modulus and UTS, however, reduces the flexibility of the nanocomposites. CMC improves flexibility but reduces UTS and modulus. GO improves % strain at break, UTS and modulus up to 1% GO, however, higher wt% of GO reduces the mechanical performance. With lower concentrations of NHS/EDC, the mechanical properties can be tailored for soft tissue engineering applications. The study highlights the importance of optimizing material compositions for tissue engineering applications.

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Tracking and Analysis of Pedestrian’s Behavior in Public Places

Cited by 6 publications

References 39 publications

Unsupervised Learning Methods for Human Abnormal Behavior Detection in Surveillance Systems; A Survey

Unsupervised Learning Methods for Human Abnormal Behavior Detection in Surveillance Systems; A Survey

Machine learning models to predict the relationship between printing parameters and tensile strength of 3D Poly (lactic acid) scaffolds for tissue engineering applications

Investigation of effect of processing parameters of 3D printed NHS/EDC crosslinked carboxy methyl cellulose/gelatin hydrogels with machine learning techniques

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