# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 2 State of the Art  
In [13], the authors use threefold cross-validation to improve HAR accuracy. Initially, they introduce a concept of two-stream ConvNet that mixes spatial and temporal networks. Secondly, they claim that despite little training data, a ConvNet can be trained on dense optical flow from several consecutive frames and yet achieve outstanding results. They finalize their presentation by demonstrating the potential application of multitask learning, which can effectively amplify the volume of training data and improve performance as a whole. The most important feature is local trajectory pooling, with spatial and temporal tubes that are coordinated across spatiotemporal layers to concentrate on trajectories. Even while the network can detect the optical flow along the trajectories, it ignores trajectories in spatial pooling. However, the designed network still needs to catch up with the current state-of-the-art shallow representation [21] in terms of the achieved accuracy. In [22], spatiotemporal ResNets were used as a combination of these two methods. To begin, residual connections between the appearance and motion channels of a two-stream architecture were injected to allow for spatiotemporal stream interaction. Then, the learned convolutional filters were applied to adjacent feature maps in time to convert pre-trained image ConvNets to spatiotemporal networks. In their subsequent paper [23], the same authors have reduced the parameters by merging the spatial and temporal networks at a convolutional layer, with no performance loss. A spatiotemporal architecture was designed for two-stream networks comprising a novel temporal and a convolutional fusion layers, which were connected to the networks. Regarding performance, the innovative design exceeded the top rank on two common benchmark datasets without significantly increasing the number of parameters. According to the findings, it was found that learning correspondence between ConvNet characteristics that are very abstract in both space and time is extremely important. In [24], remodeling of the dataset was deployed for initializing model learning by using the augmentation of data, and ResNet101 layer parameters trained on datasets such as ImageNet were used to deal with the overfitting issues caused by having less data. Deeper ConvNet was developed for learning the complexity of action. Using a disorder testing and training method, the model and procedure may provide a substantial boost in action recognition. The experiments showed that the strategy beats current top-ranked methodologies on two advanced datasets, the UCF101 [25] and the KTH action datasets [26]. The temporal network with deeper convolutional networks did not perform well compared to the appearance networks on the UCF101 dataset during the experimental evaluation. The following potential alternative might help to overcome this constraint where it was proposed to capture information on motion with a deep temporal structure by adopting deeper recurrent neural networks (RNNs). A two-stream adaptive graph convolutional network (2S-AGCN) was designed specifically for action recognition in [27], which uses the skeleton technique. In this technique, graph convolutional networks (GCNs) are used, which model the human body skeleton as spatiotemporal graphs. The backpropagation technique may learn the network architecture either uniformly or individually as it goes along. Making this data-driven technique part of the model increases the model's flexibility for constructing graphs and increases the model's generality for varying data samples. To explain both first-order and second-order information, a two-stream framework was developed, and a significant improvement in recognition accuracy was achieved. In [28], the authors proposed a motion-attentive transition network for zero-shot video object segmentation. They named this network MATNet; it uses a two-stream encoder network to treat motion and appearance independently in separate streams.