## 2 Background Study
### Transfer Learning  
With the tremendous improvement of modern technology over the past few years, we have seen much success of deep neural networks in different fields, particularly in recognition, classification, and machine translation tasks. These significant changes are also accelerated to the network architectural design such as AlexNet, ResNet, SuffleNet, GoogleNet, DenseNet, and MobileNet [36]. Even though the achievement of the deep neural network design has significant contributions in this domain, it still requires advanced knowledge and huge time to design an effective and efficient model. Due to the design and time complexity of the deep learning, we use the pretrained model trained with the ImageNet dataset for classification tasks known as transfer learning for human action recognition. We fine-tune the pretrained model to get better results for our proposed method. We consider three well-known pretrained deep learning models: MobileNetV2 [37], DenseNet121 [38], and ResNet18 [39] as the backbone of CNN models which are commonly applied in detection, recognition, and classification problems. To emphasize on the strength of STIF representation from the 3D skeleton data, we consider both less parameterized model (MobileNetV2) and heavy parameterized models (DenseNet121 and ResNet18). MobileNetV2 integrates inverted residual blocks [37] in which the shortcut connection is established between the thin bottleneck layers. In MobileNetV2, an inverted residual block with a linear bottleneck first increases the dimensionality of the feature maps from low-dimensional inputs to high-dimensional outputs. Then a light-weight depth-wise separable convolution is introduced to filter the outputs. The feature maps are processed from low-dimension to high-dimension and again back into low-dimension. The main scenario is similar to narrow-wide-narrow concepts that reduce the number of parameters significantly. Figure 1 shows an inverted residual block of MobileNetV2 in which there are three blocks. The first block performs \(1\times 1\) convolution along with batch normalization and rectified linear unit (ReLU) operations to generate wide-dimensional feature maps. This wide-dimensional feature maps are then passed through the second block that accomplishes the depth-wise \(3\times 3\) separable convolution to reduce the computation. Finally, the third block conducts \(1\times 1\) convolution and batch normalization and reduces the dimensionality of the feature maps. DenseNet (dense convolutional network) focuses on the extraction of deeper features and tries to make it more efficient for training. DenseNet consists of two basic blocks: (i) dense block and (ii) transition block [38]. Each layer in DenseNet is connected to all other deeper layers in the network. The first layer is connected to the second, third, fourth, and so on. The second layer is joined with the third, fourth, fifth, and so on. Figure 1(a) shows a dense block in DenseNet in which each layer is connected with all other deeper layers. The transition layer is inserted to reduce the dimensionality of the output features in which a batch normalization, a convolution with \(1\times 1\) kernel, and an average pooling with \(2\times 2\) kernel operations are performed. Figure 1(b) depicts the graphical representation  
Figure 1: Inverted residual block in MobileNetV2. of the transition block in the DenseNet. In our experiments, we consider DenseNet121 for human behaviors classification. ResNet (residual network) is designed by adding skip or shortcut connections from the previous layer to the current layer known as residual information. Compared with other well-known deep learning models, ResNet helps us to train up to hundreds or thousands of layers to get deeper features and improve the performance, particularly in recognition and classification tasks. The main component of ResNet is the residual blocks [39]. Figure 3 visualizes two kinds of residual blocks.