# I3D-Shufflenet Based Human Action Recognition
## 3 I3D-Shufflenet
### I3D-Shufflenet Network Framework  
#### 3.2.1 Channel Shuffle  
The channel shuffle draws on the idea of shufflenet. The shufflenet is a deep network proposed by face++. The shufflenet is a computationally efficient CNN model. It is mainly intended to be applied to mobile terminals (such as mobile phones, drones, and robots). Therefore, the shufflenet aims to achieve the best model performance with limited computing resources, which requires a good balance between speed and accuracy. The core operations of shufflenet are pointwise group convolution and channel shuffle, which greatly reduce the amount of calculations while maintaining accuracy. Group convolution is used in I3D neural network. The disadvantage of group convolution is that the output channel is only derived from a small part of input channel. The pixel-level group convolution is introduced for shuffling in order to reduce the computational complexity caused by the convolution operation. Group convolution hinders the information exchange between channels, which will result in a lack of representativeness feature generation. To solve this problem, the channel shuffling is adopted for information fusion between channels.  
A channel split operation is introduced. At the beginning of each unit, the \(c\) channels of the feature map are divided into two parts: one has \(c\)-\(c^{\prime}\) channels and another has \(c^{\prime}\) channels. To minimize the number of shuffles, one part is fixed, and another part contains three convolutions with the same number of channels. Two \(1\times 1\) convolutions are not grouped in case of the division of two groups second in the channel segmentation. Finally, the features of the two parts are concatenated so that the number of channels maintain fixed and a channel scramble is performed to ensure that the information of the two parts interact.  
The residual block is introduced through point-by-point grouping convolution and channel mixing operations. As shown in Figure 4, after point-by-point grouping convolution, the shuffle operation  
Figure 3: Module before and after the replacement. (**a**) Inception module diagram of I3D; (**b**) Convolution kernel replacement.  
Figure 2: Two-stage \(3\times 3\) instead of \(5\times 5\) convolution kernel.  
is performed to improve information flow between different groups on the bottleneck feature of the main branch. Then a smaller \(3\times 3\times 3\) and \(1\times 1\times 1\) depth separable convolution is used to reduce the amount of calculation, after a point-by-point grouping convolution, the two branch pixels are added. An average pooling operation is added to the branching for replacing the pixel-by-pixel addition operation which can expand the channel dimension with a small amount of computation.  
Through the shuffling operation of the 3D convolution layer and combining the Inception-V1 module, the channel fusion network is merged behind the 6 th inception of the \(3\times 3\times 3\)3D convolution start module in the I3D network. For the I3D model, five consecutive RGB frames are divided into 10 frames and corresponding optical flow segments. The input of the network has 10 frames apart, consecutive RGB frames and corresponding optical flow segments. Through the \(3\times 3\times 3\)3D convolutional layer with 512 output channels, the \(3\times 3\times 3\)3D maximum merge layer and the complete connection layer, the spatial and kinematic characteristics (\(5\times 7\times 7\) feature grid, corresponding to time, \(X\) and \(Y\) dimensions) before the last average merge layer of Inception can be obtained. Therefore, the I3D shuffle network can better extract image features.