Smart Surveillance System Detection Using PCA- Based Feature Extraction
Perception of vision and motion is a vast interdisciplinary field combining psychology, neurology, physiology, mathematics, computer science, physics, philosophy and more. The issue of the actual mechanism for the visual and computational perception of motion in the human are keep grow for the last decade. Each of the researchers is keep pursuit to find the ideal potion of a robust recognition and detection for video system. Clutches by illumination and pose variations, several compensation techniques were proposed to overcome these issues. However, were successful for face recognition in partly lightened faces and not for facial expression recognition (FER). Attempts were made to implement FER. However these were not focused for intruder face recognition/monitoring. They lack the region of interest (ROI, in this case face detection) while processing, which is crucial for environment such as in a car (a possibility of another person behind/beside the driver). Thus, an Automated Video Surveillance system is presented in this paper. The system aims at tracking an object in motion and classifying it as a human or non-human entity, which would help in subsequent human activity analysis based on PCA based feature extraction.
A. K. Junoh et al., "Smart Surveillance System Detection Using PCA- Based Feature Extraction", Applied Mechanics and Materials, Vol. 232, pp. 137-141, 2012