On the use of advanced correlation filters for human posture recognition

This study affords the method of using advance correlation filters in human posture recognition task. Two type of correlation filters were implemented and their efficacy evaluated. The correlation filters under consideration are Minimum Average Correlation Energy (MACE) and Unconstrained Minimum Average Correlation Energy (UMACE). Initial results prove that correlation filters offer significant potential used in posture recognition task with UMACE outperforming the MACE filter. In this research, both filters were subjected to a challenging task to recognize human posture without any restriction on the gender, clothing and posture variations. The UMACE filter performs remarkably well with an average accuracy of 89% compared to MACE filter which attained 42%.