Robust Human Interaction Recognition Using Extended Kalman Filter

Abstract

In the field of computer vision and pattern recognition, knowledge based on images of human activity has gained popularity as a research topic. Activity recognition is the process of determining human behavior based on an image. We implemented an Extended Kalman filter to create an activity recognition system here. The proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the image. To minimize noise, we use Gaussian filters. Extraction of silhouette using the statistical method. We use Binary Robust Invariant Scalable Keypoints (BRISK) and SIFT for feature extraction. The next step is to perform feature discrimination using Gray Wolf. After that, the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive characteristics. The experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88% and 0.86% recognition rate.