Abstract: Estimating temporally consistent 3D human body shape, pose, and motion from a monocular video is a challenging task due to occlusions, poor lightning conditions, complex articulated body poses, depth ambiguity, and limited availability of annotated data. Embodiments of present disclosure provide a method for temporally consistent motion estimation from monocular video. A monocular video of person(s) is captured by a weak perspective camera and spatial features of body of the persons are extracted from each frame of the video. Then, initial estimates of body shape, body pose, and features of the weak perspective camera are obtained. The spatial features and initial estimates are then aggregated to obtain spatio-temporal features by a combination of self-similarity matrices between the spatial features, pose and the camera and self-attention maps of the camera features and the spatial features. The spatio-temporal aggregated features are then used to predict shape and pose parameters of the person(s).

Abstract: State of art techniques attempt in extracting insights from eye features, specifically pupil with focus on behavioral analysis than on confidence level detection. Embodiments of the present disclosure provide a method and system for confidence level detection from eye features using ML based approach. The method enables generating overall confidence level label based on the subject's performance during an interaction, wherein the interaction that is analyzed is captured as a video sequence focusing on face of the subject. For each frame facial features comprising an Eye-Aspect ratio, a mouth movement, Horizontal displacements, Vertical displacements, Horizontal Squeezes and Vertical Peaks, are computed, wherein HDs, VDs, HSs and VPs are features that are derived from points on eyebrow with reference to nose tip of the detected face. This is repeated for all frames in the window. A Bi-LSTM model is trained using the facial features to derive confidence level of the subject.

Abstract: State of art techniques attempt in extracting insights from eye features, specifically pupil with focus on behavioral analysis than on confidence level detection. Embodiments of the present disclosure provide a method and system for confidence level detection from eye features using ML based approach. The method enables generating overall confidence level label based on the subject's performance during an interaction, wherein the interaction that is analyzed is captured as a video sequence focusing on face of the subject. For each frame facial features comprising an Eye-Aspect ratio, a mouth movement, Horizontal displacements, Vertical displacements, Horizontal Squeezes and Vertical Peaks, are computed, wherein HDs, VDs, HSs and VPs are features that are derived from points on eyebrow with reference to nose tip of the detected face. This is repeated for all frames in the window. A Bi-LSTM model is trained using the facial features to derive confidence level of the subject.