JOINT TEMPORAL SEGMENTATION AND CLASSIFICATION OF USER ACTIVITIES IN EGOCENTRIC VIDEOS

Embodiments disclose methods, systems and non-transitory computer readable medium for joint temporal segmentation and classification of user activities in an egocentric video. The method includes extracting low-level features from a live dataset based on predefined feature categories; determining at least one activity change frame from the egocentric video frames based on the extracted features; dividing the live dataset into partitions based on the activity change frame, each partition begins with a candidate frame; computing a recursive cost function at the candidate frame of each partition based on dynamic programming; determining a beginning time instant of the candidate frame based on the computation; segmenting the live dataset into multiple segments based on the determined time instant; identifying at least one activity segment that corresponds a user activity using a trained activity model based on multiple instance learning approach; and simultaneously associating a predefined activity label with the identified activity segment.