13. A non-transitory computer-readable medium comprising computer-executable instructions for joint temporal segmentation and classification of at least one user activity in an egocentric video, the non-transitory computer-readable medium comprising instructions for: receiving a live dataset including an egocentric video including at least one egocentric video sequence having a plurality of egocentric video frames; extracting a plurality of low-level features from the live dataset based on predefined feature categories; determining at least one activity change frame from the plurality of egocentric video frames based on the extracted plurality of low-level features; dividing the live dataset into a plurality of partitions based on the determined at least one activity change frame, wherein each of the plurality of partitions begins with a candidate video frame; computing a recursive cost function at the candidate video frame of each of the plurality of partitions based on dynamic programming; determining a beginning time instant of the candidate frame based on the computation; segmenting the live dataset into a plurality of segments based on the determined beginning time instant; identifying at least one activity segment that corresponds to at least one user activity among the plurality of segments using a predefined activity model being trained based on a multiple instance learning (MIL) based classifier; simultaneously associating a predefined activity label with the identified at least one activity segment; and outputting the live dataset assigned with the predefined activity label for the at least one activity segment.
13. A non-transitory computer-readable medium comprising computer-executable instructions for joint temporal segmentation and classification of at least one user activity in an egocentric video, the non-transitory computer-readable medium comprising instructions for: receiving a live dataset including an egocentric video including at least one egocentric video sequence having a plurality of egocentric video frames; extracting a plurality of low-level features from the live dataset based on predefined feature categories; determining at least one activity change frame from the plurality of egocentric video frames based on the extracted plurality of low-level features; dividing the live dataset into a plurality of partitions based on the determined at least one activity change frame, wherein each of the plurality of partitions begins with a candidate video frame; computing a recursive cost function at the candidate video frame of each of the plurality of partitions based on dynamic programming; determining a beginning time instant of the candidate frame based on the computation; segmenting the live dataset into a plurality of segments based on the determined beginning time instant; identifying at least one activity segment that corresponds to at least one user activity among the plurality of segments using a predefined activity model being trained based on a multiple instance learning (MIL) based classifier; simultaneously associating a predefined activity label with the identified at least one activity segment; and outputting the live dataset assigned with the predefined activity label for the at least one activity segment. 15. The non-transitory computer-readable medium according to claim 13 , further comprising instructions for: receiving a training dataset including an egocentric video, 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; extracting a plurality of low-level features from the training dataset based on the predefined feature categories; partitioning the training dataset into a plurality of overlapping segments of predefined durations; defining 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; determining a set of low-level features that belongs to the plurality of subsets, wherein the set is derived from the extracted plurality of low-level features; grouping 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; comparing each of the plurality of instances with the predefined activity label; assigning 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 training 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.