Aljuaid Hanan, Akhter Israr, Alsufyani Nawal, Shorfuzzaman Mohammad, Alarfaj Mohammed, Alnowaiser Khaled, Jalal Ahmad, Park Jeongmin
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Department of Computer Science, Bahria University, Islamabad, Pakistan.
PeerJ Comput Sci. 2023 May 24;9:e1355. doi: 10.7717/peerj-cs.1355. eCollection 2023.
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.
创新技术以及智能机械、交通设施、应急系统和教育服务的改进定义了现代时代。理解场景、进行人群分析和观察人员变得困难。对于基于电子学习的通过多层感知器对人群数据进行多目标跟踪和预测框架，本文推荐一种有组织的方法，该方法将基于电子学习人群的类型数据作为输入，基于正常和异常的行为及活动。之后，对于特征提取，我们使用超像素和模糊c均值，融合密集光流和梯度补丁，对于多目标跟踪，我们应用压缩跟踪算法和泰勒级数预测跟踪方法。下一步是找到用于轨迹提取的均值、方差、速度和帧占有率。为了降低数据复杂性并进行优化，我们应用了T分布随机邻域嵌入（t-SNE）。为了预测基于电子学习的人群数据中的正常和异常行为，我们使用多层感知器（MLP）对多个类别进行分类。我们使用了三个基于人群活动的数据集，即加利福尼亚大学圣地亚哥分校儿科学系（USCD-Ped）、上海科技大学和印度理工学院孟买分校（IITB）走廊数据集，用于基于人类和非人类视频的实验评估。我们在USCD-Ped数据集上实现了87.00%的平均准确率，在上海科技大学数据集上为85.75%，在IITB走廊数据集上为88.00%。
