Postures anomaly tracking and prediction learning model over crowd data analytics
Innovative technology and improvements in intelligent machinery, transportation facilities, emergency systems, and educational services define the modern era. It is difficult to comprehend the scenario, do crowd analysis, and observe persons. For e-learning-based multiobject tracking and predication...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-05-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1355.pdf |
| _version_ | 1827940679329775616 |
|---|---|
| author | Hanan Aljuaid Israr Akhter Nawal Alsufyani Mohammad Shorfuzzaman Mohammed Alarfaj Khaled Alnowaiser Ahmad Jalal Jeongmin Park |
| author_facet | Hanan Aljuaid Israr Akhter Nawal Alsufyani Mohammad Shorfuzzaman Mohammed Alarfaj Khaled Alnowaiser Ahmad Jalal Jeongmin Park |
| author_sort | Hanan Aljuaid |
| collection | DOAJ |
| description | Innovative technology and improvements in intelligent machinery, transportation facilities, emergency systems, and educational services define the modern era. It is difficult to comprehend the scenario, do crowd analysis, and observe persons. For e-learning-based multiobject tracking and predication framework for crowd data via multilayer perceptron, this article recommends an organized method that takes e-learning crowd-based type data as input, based on usual and abnormal actions and activities. After that, super pixel and fuzzy c mean, for features extraction, we used fused dense optical flow and gradient patches, and for multiobject tracking, we applied a compressive tracking algorithm and Taylor series predictive tracking approach. The next step is to find the mean, variance, speed, and frame occupancy utilized for trajectory extraction. To reduce data complexity and optimization, we applied T-distributed stochastic neighbor embedding (t-SNE). For predicting normal and abnormal action in e-learning-based crowd data, we used multilayer perceptron (MLP) to classify numerous classes. We used the three-crowd activity University of California San Diego, Department of Pediatrics (USCD-Ped), Shanghai tech, and Indian Institute of Technology Bombay (IITB) corridor datasets for experimental estimation based on human and nonhuman-based videos. We achieve a mean accuracy of 87.00%, USCD-Ped, Shanghai tech for 85.75%, and IITB corridor of 88.00% datasets. |
| first_indexed | 2024-03-13T09:14:02Z |
| format | Article |
| id | doaj.art-4e3f238c99814b02967a88c225fe95e8 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-13T09:14:02Z |
| publishDate | 2023-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-4e3f238c99814b02967a88c225fe95e82023-05-26T15:05:07ZengPeerJ Inc.PeerJ Computer Science2376-59922023-05-019e135510.7717/peerj-cs.1355Postures anomaly tracking and prediction learning model over crowd data analyticsHanan Aljuaid0Israr Akhter1Nawal Alsufyani2Mohammad Shorfuzzaman3Mohammed Alarfaj4Khaled Alnowaiser5Ahmad Jalal6Jeongmin Park7 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science, Bahria University, Islamabad, PakistanDepartment of Computer Science, Taif University, Taif, Saudi ArabiaDepartment of Computer Science, Taif University, Taif, Saudi ArabiaDepartment of Electrical Engineering, King Faisal University, Al-Ahsa, Saudi ArabiaDepartment of Computer Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer Science, Air University, Islamabad, PakistanDepartment of Computer Engineering, Tech University of Korea, Sangidaehak-ro, Siheung-si, South KoreaInnovative technology and improvements in intelligent machinery, transportation facilities, emergency systems, and educational services define the modern era. It is difficult to comprehend the scenario, do crowd analysis, and observe persons. For e-learning-based multiobject tracking and predication framework for crowd data via multilayer perceptron, this article recommends an organized method that takes e-learning crowd-based type data as input, based on usual and abnormal actions and activities. After that, super pixel and fuzzy c mean, for features extraction, we used fused dense optical flow and gradient patches, and for multiobject tracking, we applied a compressive tracking algorithm and Taylor series predictive tracking approach. The next step is to find the mean, variance, speed, and frame occupancy utilized for trajectory extraction. To reduce data complexity and optimization, we applied T-distributed stochastic neighbor embedding (t-SNE). For predicting normal and abnormal action in e-learning-based crowd data, we used multilayer perceptron (MLP) to classify numerous classes. We used the three-crowd activity University of California San Diego, Department of Pediatrics (USCD-Ped), Shanghai tech, and Indian Institute of Technology Bombay (IITB) corridor datasets for experimental estimation based on human and nonhuman-based videos. We achieve a mean accuracy of 87.00%, USCD-Ped, Shanghai tech for 85.75%, and IITB corridor of 88.00% datasets.https://peerj.com/articles/cs-1355.pdfAnomaly detectionCompressive tracking AlgorithmCrowd based dataData optimizationE-LearningFused dense optical flow |
| spellingShingle | Hanan Aljuaid Israr Akhter Nawal Alsufyani Mohammad Shorfuzzaman Mohammed Alarfaj Khaled Alnowaiser Ahmad Jalal Jeongmin Park Postures anomaly tracking and prediction learning model over crowd data analytics PeerJ Computer Science Anomaly detection Compressive tracking Algorithm Crowd based data Data optimization E-Learning Fused dense optical flow |
| title | Postures anomaly tracking and prediction learning model over crowd data analytics |
| title_full | Postures anomaly tracking and prediction learning model over crowd data analytics |
| title_fullStr | Postures anomaly tracking and prediction learning model over crowd data analytics |
| title_full_unstemmed | Postures anomaly tracking and prediction learning model over crowd data analytics |
| title_short | Postures anomaly tracking and prediction learning model over crowd data analytics |
| title_sort | postures anomaly tracking and prediction learning model over crowd data analytics |
| topic | Anomaly detection Compressive tracking Algorithm Crowd based data Data optimization E-Learning Fused dense optical flow |
| url | https://peerj.com/articles/cs-1355.pdf |
| work_keys_str_mv | AT hananaljuaid posturesanomalytrackingandpredictionlearningmodelovercrowddataanalytics AT israrakhter posturesanomalytrackingandpredictionlearningmodelovercrowddataanalytics AT nawalalsufyani posturesanomalytrackingandpredictionlearningmodelovercrowddataanalytics AT mohammadshorfuzzaman posturesanomalytrackingandpredictionlearningmodelovercrowddataanalytics AT mohammedalarfaj posturesanomalytrackingandpredictionlearningmodelovercrowddataanalytics AT khaledalnowaiser posturesanomalytrackingandpredictionlearningmodelovercrowddataanalytics AT ahmadjalal posturesanomalytrackingandpredictionlearningmodelovercrowddataanalytics AT jeongminpark posturesanomalytrackingandpredictionlearningmodelovercrowddataanalytics |