9. The system according to claim 7 , further comprises a training module on the computer configured to: receive a training dataset including an egocentric video via the data input module, wherein the training dataset is pre-segmented based on the predefined activity label that corresponds to the at least one user activity having a predefined time length; partition the training dataset into a plurality of overlapping segments of predefined durations; define a plurality of subsets, each including a distinct combination of overlapping segments from the plurality of overlapping segments based on a duration of the distinct combination being less than half of the predefined time length; determine a set of low-level features that belong to the plurality of subsets, wherein the set is extracted using the feature extraction module based on the predefined feature categories; group the plurality of subsets into one or more clusters based on the determined set of low-level features, wherein the one or more clusters include at least one centroid that forms a bag including a plurality of instances; compare each of the plurality of instances with the predefined activity label; assign the bag with a positive value if the comparison is positive for at least one of the plurality of instances, or with a negative value if the comparison is negative for all the one or more instances; and train the predefined activity model using the assigned value of the bag based on the multiple instance learning (MIL) based classifier, wherein the activity model corresponds to the at least one user activity in the training dataset.
9. The system according to claim 7 , further comprises a training module on the computer configured to: receive a training dataset including an egocentric video via the data input module, wherein the training dataset is pre-segmented based on the predefined activity label that corresponds to the at least one user activity having a predefined time length; partition the training dataset into a plurality of overlapping segments of predefined durations; define a plurality of subsets, each including a distinct combination of overlapping segments from the plurality of overlapping segments based on a duration of the distinct combination being less than half of the predefined time length; determine a set of low-level features that belong to the plurality of subsets, wherein the set is extracted using the feature extraction module based on the predefined feature categories; group the plurality of subsets into one or more clusters based on the determined set of low-level features, wherein the one or more clusters include at least one centroid that forms a bag including a plurality of instances; compare each of the plurality of instances with the predefined activity label; assign the bag with a positive value if the comparison is positive for at least one of the plurality of instances, or with a negative value if the comparison is negative for all the one or more instances; and train the predefined activity model using the assigned value of the bag based on the multiple instance learning (MIL) based classifier, wherein the activity model corresponds to the at least one user activity in the training dataset. 12. The system according to claim 9 , wherein the positive value is a non-unity value and the negative value is a unity value.