Application for video analysis based on machine learning and computer vision algorithms

An application for video data analysis based on computer vision methods is presented. The proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis. AdaBoost classifier is utilized for face detection. A modification of Lucas and Kanade algorithm is introduced on the stage of tracking. Novel gender and age classifiers based on adaptive features and support vector machines are proposed. All the stages are united into a single system of audience analysis. The proposed software complex can find its applications in different areas, from digital signage and video surveillance to automatic systems of accident prevention and intelligent human-computer interfaces

Gender classification for real-time audience analysis system

The system allowing to extract all the possible information about depicted people from the input video stream is discussed. As reported previously, the proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis. The crucial part of the system is gender classifier construction on the basis of machine learning methods. We propose a novel algorithm consisting of two stages: adaptive feature extraction and support vector machine classification. Both training technique of the proposed algorithm and experimental results acquired on a large image dataset are presented. More than 90% accuracy of viewer's gender recognition is achieved