Open AccessSpatial Attention Adapted to a LSTM Architecture with Frame Selection for Human Action Recognition in Videos
Author(s) -
Carlos Orozco,
María Elena Buemi,
Julio Jacobo Berllés
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.52591/2021072411
Subject(s) - computer science , search engine indexing , metric (unit) , artificial intelligence , action recognition , frame (networking) , architecture , selection (genetic algorithm) , action (physics) , pattern recognition (psychology) , computer vision , machine learning , telecommunications , engineering , physics , quantum mechanics , class (philosophy) , art , operations management , visual arts
Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. In this work we propose an attention mechanism adapted to a CNN–LSTM base architecture. To carry out the training and testing phases, we used the HMDB-51 and UCF-101 datasets. We evaluate the performance of our system using accuracy as the evaluation metric, obtaining 57.3% and 90.4% for HMDB-51 an UCF-101 respectively