Marker-Based and Marker-Less Motion Capturing Video Data: Person and Activity Identification Comparison Based on Machine Learning Approaches

作     者：Syeda Binish Zahra Muhammad Adnan Khan Sagheer Abbas Khalid Masood Khan Mohammed A.Al-Ghamdi Sultan H.Almotiri 

作者机构：School of Computer ScienceNational College for Business Administration and EconomicsLahore54000Pakistan Department of Computer ScienceLahore Garrison UniversityLahore54792Pakistan Computer Science DepartmentUmm Al-Qura UniversityMakkah CitySaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第66卷第2期

页      面：1269-1282页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Data and Artificial Intelligence Scientific Chair at Umm Al-Qura University 

主　　题：Marker-based motion capturing system marker-less motion capturing system support vector machine K-nearest neighbor 

摘      要：Biomechanics is the study of physiological properties of data and the measurement of human *** normal conditions,behavioural properties in stable form are created using various inputs of subconscious/conscious human activities such as speech style,body movements in walking patterns,writing style and voice *** cannot perform any change in these inputs that make results reliable and increase the *** aim of our study is to perform a comparative analysis between the marker-based motion capturing system(MBMCS)and the marker-less motion capturing system(MLMCS)using the lower body joint angles of human gait *** both the MLMCS and MBMCS,we collected trajectories of all the participants and performed joint angle computation to identify a person and recognize an activity(walk and running).Using five state of the art machine learning algorithms,we obtained 44.6%and 64.3%accuracy in person identification using MBMCS and MLMCS respectively with an ensemble algorithm(two angles as features).In the second set of experiments,we used six machine learning algorithms to obtain 65.9%accuracy with the k-nearest neighbor(KNN)algorithm(two angles as features)and 74.6%accuracy with an ensemble ***,by increasing features(6 angles),we obtained higher accuracy of 99.3%in MBMCS for person recognition and 98.1%accuracy in MBMCS for activity recognition using the KNN *** is computationally expensive and if we redesign the model of OpenPose with more body joint points and employ more features,MLMCS(low-cost system)can be an effective approach for video data analysis in a person identification and activity recognition process.