Human Behavior Analysis Based on Multi-Types Features Fusion and Von Nauman Entropy Based Features Reduction

Human Behavior Recognition (HBR) or Human Action Recognition (HAR) is an important area of research having numerous applications in the field of computer vision and machine learning. In this article, we proposed a new method for HBR based on multi-types features fusion and irrelevant features reduction named MtFR. The Proposed MtFR approach initially selects a luminance channel and calculates motion estimation using optical flow. Afterwards, the moving regions are extracted through background subtraction approach. In the features extraction step, shape, color, and Gabor wavelet features are extracted and fused based on serial method. Thereafter, reduced irrelevant and redundant features are removed by Von Neuman entropy approach. The selected reduced features are finally recognized by One-Against-All (OAA) Multi-class SVM classifier. Extensive experiments are performed using three famous datasets such as Muhavi, WVU and YouTube, and achieved the recognition rate of 99.2%, 99.3%, and 100%, respectively.