Based on deep models, video action recognition typically involves sampling the input video and then extracting features from the obtained video frames to classify actions. Therefore, the video frame sampling method directly affects the effectiveness of action recognition. Aiming to sample key and effective features while enhanced the motion information in videos, this paper proposed a LGMeNet based on a feature-level sampling strategy. Firstly, it used a feature-level sampling module to uniformly select frames with the same motion information from the input data. Secondly, it employed a local motion feature extraction module to compute short-term motion features using a similarity function. Finally, it utilized a LSTM network in the global motion feature extraction module to calculate multi-scale long-term motion features. Experimental evaluations show that LGMeNet achieves accuracies of 97.7% and 56.9% on the UCF101 and Something-SomethingV1 datasets, respectively. The results of this study demonstrate the effectiveness of LGMeNet in enhancing action recognition and highlight its significance for further advancements in related research areas.

[2023-07-25] Accepted Paper /doc/2023.05.0216/v1

[2023-12-05] Printed Article /doc/2023.05.0216/v2