Analysis of Multi-Scale Fractal Dimension to Classify Human Motion
This is an incremental approach to human action recognition that could potentially benefit applications like security systems, traffic monitoring, and healthcare.
This study tackled the problem of human motion classification in videos by investigating the use of 3D multi-scale fractal dimension to recognize motion patterns, achieving varying accuracy rates across different databases.
In recent years there has been considerable interest in human action recognition. Several approaches have been developed in order to enhance the automatic video analysis. Although some developments have been achieved by the computer vision community, the properly classification of human motion is still a hard and challenging task. The objective of this study is to investigate the use of 3D multi-scale fractal dimension to recognize motion patterns in videos. In order to develop a robust strategy for human motion classification, we proposed a method where the Fourier transform is used to calculate the derivative in which all data points are deemed. Our results shown that different accuracy rates can be found for different databases. We believe that in specific applications our results are the first step to develop an automatic monitoring system, which can be applied in security systems, traffic monitoring, biology, physical therapy, cardiovascular disease among many others.