# Action Recognition Network Based on Local Spatiotemporal Features and Global Temporal Excitation
## 2 Related Work  
Many researchers have begun to apply deep learning technology in the tasks of video action recognition because it brings a huge improvement in the field of images. A wide range of deep-learning-based action recognition methods have been suggested by numerous researchers, outperforming traditional methods in performance. Methods based on 3 DCNN. Videos contain an additional temporal dimension compared with images, making it more challenging to collect information from videos. 3 DCNN-based action recognition methods have been suggested to recognize actions in videos by extracting spatial and temporal features. C3D [10] applied 3D convolution to extract temporal and spatial features from videos by convolving on adjacent frames. T3D [11] suggested a new migration learning method, which migrates the weight parameters of the previously trained 2D CNN to the 3D CNN, and captured time information at different time scales. SlowFast [7] used dual path network to sample frame sequences at unequal sampling speeds to extract spatial and temporal features separately. The slow path extracted spatial features, while the fast path extracted temporal features. Although these methods based on 3 DCNN architecture have achieved good recognition results, they have some drawbacks such as a lot of parameters and slow convergence speed. To lower the computational cost of 3D CNN, 53D [13], P3D [14], and other similar methods decomposed the 3D convolution into 2D convolution, for extracting spatial information, and 1D convolution for extracting temporal information. ECO [15], GST [23] mixed 2D convolution, and 3D convolution are used in a network to improve computational efficiency. In contrast, our LSCT network utilizes temporal modeling modules to enhance the performance of 2D CNNs, which does not result in a large computational cost. Methods based on 2 DCNN. [PERSON] et al. [24] proposed a 2D CNNs-based action recognition network that utilized a multi-resolution approach and was trained on the Sports-1M dataset. It used RGB frames as input, and tested various methods to combine temporal information. However, it performed poorly since it was unable to accurately capture the motion information of neighboring frames. [PERSON] et al. [3] suggested a two-stream network to recognize actions in videos, where spatial stream with a RGB frame extracted spatial features while the flow stream with optical flow information extracted local motion features. However, extracting the optical flow was computationally expensive and required separate extraction, preventing end-to-end learning. [PERSON] [16] proposed a segmented sampling strategy, which split the video into fixed segments equally and randomly captured a frame from each segment to feed the network. However, it lacked local temporal modeling and relied heavily on precomputed optical flow to extract local motion information. Based on this sampling strategy, subsequent studies have proposed multiple temporal modeling modules that were embedded in 2D CNNs to effectively model temporal information. TSM [17] designed a time shift module to exchange the temporal information of neighboring video frames by shifting partial channels in the time dimension. This module was embedded in 2D CNNs to model temporal features of video sequences. It has achieved high recognition performance with relatively little computational cost. TEI [19] employed an attention mechanism that utilized motion information to enhance motion-related features and leveraged a time shift module to model temporal features. STM [25], TIN [26], TEA [20], and TAM [27] etc. designed rich temporal modules which were embedded into 2 DCNNs to effectively recognize actions in videos. The methods discussed above utilize the segmented sampling approach suggested by TSN to sample video sequences and have achieved good recognition results. However, the local motion information within each segment was ignored by this sampling strategy since it only selected one frame from each segment.