Academic literature on the topic 'MULTIVIEW HUMAN GAIT'

This paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based gait recognition system with joint detection approach that performs well under different walking trajectories and covariate factors, which include self-occluded or external occluded silhouettes. In the proposed system, the process begins by enhancing the human silhouette to remove the artifacts. Next, the width and height of the body are obtained. Subsequently, the joint angular trajectories are determined once the body joints are automatically detected. Lastly, crotch height and step-size of the walking subject are determined. The extracted features are smoothened by Gaussian filter to eliminate the effect of outliers. The extracted features are normalized with linear scaling, which is followed by feature selection prior to the classification process. The classification experiments carried out on MMUGait database were benchmarked against the SOTON Small DB from University of Southampton. Results showed correct classification rate above 90% for all the databases. The proposed approach is found to outperform other approaches on SOTON Small DB in most cases.

Nowadays, RPA robots are increasingly used in daily office tasks such as finance and human resources. They play an increasingly important role in realizing office automation, which can improve work efficiency and reduce labor costs. In order to improve the efficiency of budget management and save human resources, this paper conducts related research based on the multiview recognition technology of network communication integration, combined with RPA in artificial intelligence technology. In the method part, this article introduces the mode of network communication integration and the principles that should be followed, as well as the related processes of RPA. In the algorithm, this paper introduces an integrated algorithm based on ELM. In the experimental part, this article predicts the performance of each model, compares identification functions with different signal-to-voice signals, and compares timing functions on different signal-to-voice signals, periodic transmission mode indicators, recognition rates of different kernel functions, and comparison of average recognition rates and multiview recognition rate comprehensive analysis of these multiple aspects. Under the same conditions, the recognition rate of some angles is lower than other angles; 0 degrees, 18 degrees, 126 degrees, and 180 degrees are slightly lower than other angles, which will affect the average recognition rate of the entire recognition. But for multiview gait features, considering the influence of each angle on the recognition rate, the characteristics of each angle are merged together, so that the recognition rate is significantly higher than the average recognition rate of 11 angles. It can be seen that multiview recognition based on network communication integration does have obvious effects on RPA and artificial intelligence in budget management and can improve the efficiency of budget management. The multiperspective recognition technology designed in this study can realize modernization and digitization in budget management.

The gait of a person is often used as a biometric tool to identify or classify a person based on gender and age. Because of its ability to track a person from afar, gait recognition has found use in a variety of fields, including forensics, surveillance, and health monitoring departments. Biometric systems is a rapidly emerging area that necessitates the development of new methods to address problems that have plagued previous approaches. Human gait is a less-explored region in the field of biometrics. In this Project, two different types of gait datasets have been reported and presented. The FP (First Person) data carrying the camera motion collected from the movement of the volunteer's body and the TP (Third Person) data captured from a distant view were recorded at the same time. A total of 24 subjects (15 males and 9 females) are included in the dataset. The discussion is extended to include a comparison of the results obtained using TP and FP data. This report also provides an extensive survey of the stages involved in the framework of gait recognition by analysing the different methods used in each stage along with the description of the feature extraction process and the state-of-the-art techniques used in appearance-based and human-pose-based methods. Moreover, a brief comparative description on the recent data reduction or feature selection methods has been provided. Furthermore, it will provide a first-hand knowledge about the public datasets that is motion capture databases and the datasets simply used for human gait recognition. In comparison to other biometric methods, gait recognition has a lot of potential for future work, Researchers working in the fields of biometrics, human pose estimation, monitoring, human gait recognition and analysis will benefit from the review given in the survey.