TY - GEN
T1 - Towards practical compressed video action recognition
T2 - 25th International Conference on Pattern Recognition, ICPR 2020
AU - Li, Bing
AU - Kong, Longteng
AU - Zhang, Dongming
AU - Bao, Xiuguo
AU - Huang, Di
AU - Wang, Yunhong
N1 - Publisher Copyright:
© 2020 IEEE
PY - 2020
Y1 - 2020
N2 - Current compressed video action recognition methods are mainly based on complete data. However, in a real transmission scenario, the compressed video packets are usually disorderly received and even lost due to network jitters or congestion. To recognize actions in early phases with limited packets, e.g. for quickly forecasting possible potential risks, in this paper, we propose a Temporal Enhanced Multi-Stream Network (TEMSN) towards practical compressed video action recognition. First, we make use of three modalities in the compressed domain as complementary cues and build a multi-stream network to capture rich information from compressed video packets. Second, we design a temporal enhanced module based on an Encoder-Decoder structure, which is applied to each stream to infer missing packets, generating more accurate action dynamics. Thanks to the multiple modalities and their temporal enhancement, our approach better models actions with partial available compressed video packets. Experiments on the HMDB-51 and UCF-101 datasets validate its effectiveness and efficiency.
AB - Current compressed video action recognition methods are mainly based on complete data. However, in a real transmission scenario, the compressed video packets are usually disorderly received and even lost due to network jitters or congestion. To recognize actions in early phases with limited packets, e.g. for quickly forecasting possible potential risks, in this paper, we propose a Temporal Enhanced Multi-Stream Network (TEMSN) towards practical compressed video action recognition. First, we make use of three modalities in the compressed domain as complementary cues and build a multi-stream network to capture rich information from compressed video packets. Second, we design a temporal enhanced module based on an Encoder-Decoder structure, which is applied to each stream to infer missing packets, generating more accurate action dynamics. Thanks to the multiple modalities and their temporal enhancement, our approach better models actions with partial available compressed video packets. Experiments on the HMDB-51 and UCF-101 datasets validate its effectiveness and efficiency.
UR - https://www.scopus.com/pages/publications/85110540678
U2 - 10.1109/ICPR48806.2021.9412213
DO - 10.1109/ICPR48806.2021.9412213
M3 - 会议稿件
AN - SCOPUS:85110540678
T3 - Proceedings - International Conference on Pattern Recognition
SP - 3744
EP - 3750
BT - Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 January 2021 through 15 January 2021
ER -