%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2020/09.30.02.16
%2 sid.inpe.br/sibgrapi/2020/09.30.02.16.07
%@doi 10.1109/SIBGRAPI51738.2020.00017
%T Faster and Accurate Compressed Video Action Recognition Straight from the Frequency Domain
%D 2020
%A Santos, Samuel Felipe dos,
%A Almeida, Jurandy,
%@affiliation Universidade Federal de São Paulo - UNIFESP
%@affiliation Universidade Federal de São Paulo - UNIFESP
%E Musse, Soraia Raupp,
%E Cesar Junior, Roberto Marcondes,
%E Pelechano, Nuria,
%E Wang, Zhangyang (Atlas),
%B Conference on Graphics, Patterns and Images, 33 (SIBGRAPI)
%C Porto de Galinhas (virtual)
%8 7-10 Nov. 2020
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K action recognition, convolutional neural network, compressed-domain processing, frequency domain.
%X Human action recognition has become one of the most active field of research in computer vision due to its wide range of applications, like surveillance, medical, industrial environments, smart homes, among others. Recently, deep learning has been successfully used to learn powerful and interpretable features for recognizing human actions in videos. Most of the existing deep learning approaches have been designed for processing video information as RGB image sequences. For this reason, a preliminary decoding process is required, since video data are often stored in a compressed format. However, a high computational load and memory usage is demanded for decoding a video. To overcome this problem, we propose a deep neural network capable of learning straight from compressed video. Our approach was evaluated on two public benchmarks, the UCF-101 and HMDB-51 datasets, demonstrating comparable recognition performance to the state-of-the-art methods, with the advantage of running up to 2 times faster in terms of inference speed.
%@language en
%3 PID6630911.pdf