Motion Balance Ability Detection Based on Video Analysis in Virtual Reality Environment

• Main Authors: Jilan Zhou, Yang You, Yanmin Zhao
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Movement target balance ability detection; movement balance ability; balance acquisition; VR~environment
• Online Access: https://ieeexplore.ieee.org/document/9178336/

In recent years, smart camera devices under the Virtual Reality (VR) environment have been widely popularized. These devices can be equipped with fast and effective computer vision applications, including the detection of the balance ability of moving targets. Moving target balance ability detection plays an important role in public security, traffic monitoring and other fields, and is also a basic technology for many vision applications. Therefore, the requirements for accuracy and completeness of detection are getting higher and higher. This article proposes a tracking method Motion Model and Model Updater (MMMU) based on the balance acquisition and model update and intelligent adjustment of the motion model. Improved Motion Model (IMM) is a background sample balance acquisition algorithm based on simple linear iterative clustering, completes the abstraction of background images. Different from other update strategies with a fixed number of frames, the update strategy based on image histogram contrast relies on the human selective forgetting mechanism to better avoid burst frames and process similar frames. Since the data used to detect the balance ability of moving targets is inherently unbalanced, the idea of dealing with imbalance in data mining is introduced into it, and the problem of balance ability detection of moving targets is studied from the perspectives of downsampling and oversampling. In addition, temporal and spatial oversampling of the foreground and selective downsampling of the background are performed to reduce the imbalance of the data set, and the resampled data set is used for modeling and classification. The feasibility of the MMMU algorithm is tested through experiments, and the motion balance ability of the foreground target is detected relatively completely.