Pose Estimation and Detection for Event Recognition using Sense-Aware Features and Adaboost Classifier

To examine events identification and recognition in sequential images, approaches used several parameters such as size, location or position of the human body parts along with its surrounding effects. In this paper, we estimated several body key points to monitor and track its appearance in complex events. Such detection of key body parts need several features descriptor such as optical flow, moving body parts and 0-180° intensity characteristics for event identification. While, extraction architecture model used human body key points through the R-Transform factor having intensity values. After intensity values extraction, the estimation of moveable body parts are applied for change detection system. In addition, optical flow features are also extracted. The extracted intensity and moveable body features are merged with optical flow vectors. These extracted features are injected into pre-classifier as Particle Swarm optimization and recognizer engine as Adaboost. The experimental results on a challenging videos datasets such as UCF 101 and YouTube show significant accuracy 75.33% and 76.66% of event recognition. We achieved higher performance of body-parts detection and event recognition as compare to state-of-art approaches.