Vr-Hmt: Virtual Reality-Driven Human Motion Tracking System via Mask Rcnn for Real-Time Monitoring

Human motion tracking (HMT) is capturing and analyzing the movement of individuals and objects, focusing on their location, speed, and acceleration. It is used in various fields including filmmaking, animation, sports analysis, robotics, and augmented reality. Unauthorized physical access to facilities presents significant challenges for organizations as it can result in theft, sabotage, and data breaches, these incidents lead to financial losses, and compromised sensitive information. To overcome these challenges, a novel virtual reality (VR) based XXX system has been proposed for tracking human motion to identify any suspicious activity within an organization. The system uses various sensors such as cameras and motion sensors to capture real-world activity in a monitored space. These sensors capture continuous video, which has been converted into frames for further processing. The bilateral filter is used as a pre-processing step to minimize sound while maintaining edges in the frames. The Mask R-CNN model is used for HMT from the frames assigns bounding boxes around each detected human and predicts segmentation masks on each detected frame. The tracking data is stored in a database server and displayed on a large screen to observe the environment. The processed data is further transferred to an application server which serves as a control unit for managing the data and coordinating with the central platform. According to the result, the proposed model attains a 98.45% accuracy rate for human motion tracking. The proposed XXX model achieves an overall accuracy of 2.18%, 8.99%, and 1.47% compared to the existing method such as DBSCAN, cluster segmentation, and k-fold cross-validation.