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LEARNING SPATIO-TEMPORAL FEATURE EXTRACTION USING RESIDUAL FRAMES WITH NEURALNETWORKS FOR HUMAN ACTION RECOGNITION
2022
Zenodo
In recent times the growth of machine learning and artificial intelligence algorithms help to expand the use of image and video processing. The usage of different algorithms is applicable in various fields such as content-based video recognition, video surveillance, assistive living, autism care, and gaming. HAR (Human Action Recognition) highly demands efficient computation. This research proposed a method for selecting residual frames and keyframes toeliminate redundant information from
doi:10.5281/zenodo.6655088
fatcat:qsto4ftp5rdofp7lfzqzucigoe