A processor implemented method, comprising: receiving, via one or more hardware processors, a video stream of a scene from an egocentric camera embodied in a robot, the video stream comprising a plurality of image frames; identifying, via the one or more hardware processors, one or more groups of human subjects in the scene, wherein identifying the one or more groups comprises: determining, using a deep learning-based model, a plurality of human body skeletons in the input images; and predicting a plurality of key-points for each of the plurality of human body skeletons; associating, by the deep learning model, a plurality of confidence scores with the plurality of key-points, wherein a confidence score from amongst the plurality of confidence scores is associated with a key-point from amongst the plurality of key-points based on an orientation of a person of whom the human body skeleton is detected by the deep learning based model; determining a plurality of features by categorizing the plurality of key-points with respect to the plurality of confidence scores into a plurality of labels, via the one or more hardware processors; predicting, by a conditional random field, CRF, based probabilistic model, for each group of the one or more groups, one or more members of the group and one or more outliers of the group, via the one or more hardware processors, the CRF model utilizing the plurality of features; creating via the one or more hardware processors, a data set from the CRF based probabilistic model using a camera mounted on the robot for one or more real life formations, the one or more real life formations being at least one of a face-to-face, side by side, L-shaped and triangular from various angles and distances; predicting, via the one or more hardware processors, an f-formation in the each group using a multi-class Support Vector Machine, SVM, with a Gaussian Radial Basis Function, RBF, kernel based on the one or more members of the group, wherein one or more encoded skeletal key points are used to train the SVM for detection of the f-formation; and determining, via the one or more hardware processors, an approach angle of the robot to join the group using the multi-class SVM with the Gaussian RBF kernel, and wherein an orientation of the F-formation is detected with respect to the robot, wherein the F-formation and the approach angle are jointly predicted in a single classifier. receiving, via one or more hardware processors, a video stream of a scene from an egocentric camera embodied in a robot, the video stream comprising a plurality of image frames; identifying, via the one or more hardware processors, one or more groups of human subjects in the scene, wherein identifying the one or more groups comprises: determining, using a deep learning-based model, a plurality of human body skeletons in the input images; and predicting a plurality of key-points for each of the plurality of human body skeletons; determining, using a deep learning-based model, a plurality of human body skeletons in the input images; and predicting a plurality of key-points for each of the plurality of human body skeletons; associating, by the deep learning model, a plurality of confidence scores with the plurality of key-points, wherein a confidence score from amongst the plurality of confidence scores is associated with a key-point from amongst the plurality of key-points based on an orientation of a person of whom the human body skeleton is detected by the deep learning based model; determining a plurality of features by categorizing the plurality of key-points with respect to the plurality of confidence scores into a plurality of labels, via the one or more hardware processors; predicting, by a conditional random field, CRF, based probabilistic model, for each group of the one or more groups, one or more members of the group and one or more outliers of the group, via the one or more hardware processors, the CRF model utilizing the plurality of features; creating via the one or more hardware processors, a data set from the CRF based probabilistic model using a camera mounted on the robot for one or more real life formations, the one or more real life formations being at least one of a face-to-face, side by side, L-shaped and triangular from various angles and distances; predicting, via the one or more hardware processors, an f-formation in the each group using a multi-class Support Vector Machine, SVM, with a Gaussian Radial Basis Function, RBF, kernel based on the one or more members of the group, wherein one or more encoded skeletal key points are used to train the SVM for detection of the f-formation; and determining, via the one or more hardware processors, an approach angle of the robot to join the group using the multi-class SVM with the Gaussian RBF kernel, and wherein an orientation of the F-formation is detected with respect to the robot, wherein the F-formation and the approach angle are jointly predicted in a single classifier.