# Action Recognition Algorithm of Spatio-Temporal Differential LSTM Based on Feature Enhancement
## 4 Experiments
### Datasets  
Research teams, both overseas and domestic, usually use human action datasets in algorithm training to detect the algorithm's accuracy and robustness. The dataset has at least the following two essential functions:  
1. The researchers do not have to consider the process of collection and pretreatment.
2. It is able to compare different algorithms under the same standard.  
The KTH dataset [21] was released in 2004. The KTH dataset includes six kinds of actions (including strolling, jogging, running, boxing, waving, and clapping) performed by 25 people in 4 different scenes. The dataset has 2391 video samples and includes scale transformation, clothing transformation, and lighting transformation. However, the shooting camera is fixed, and the background is similar.  
The Weizmann dataset [22] was released in 2005 and includes nine people completing ten kinds of actions (bending, stretching, high jump, jumping, running, standing, hopping, walking, waving1, and waving). In addition to category tags, the dataset contains silhouettes of people in the foreground and background sequences to facilitate background extraction. However, the dataset has a fixed perspective and simple backgrounds.  
The above two datasets are released early. The citation rate of these datasets is high. However, with the rapid development of action recognition, there are shortcomings: the  
Figure 2: The connection mode between ST-D LSTM units.  
background is simple, the angle is fixed, and each video has only one person. The above two datasets already cannot satisfy actual action recognition requirements, so they are rarely used now.  
The Hollywood2 dataset [23] was released in 2009. The video data in the dataset are collected from Hollywood movies. There are 3669 video clips in total, including 12 action categories (such as: answering the phone, eating, driving, etc.) extracted from 69 movies and 10 scenes (outdoor, shopping mall, kitchen, etc.). The dataset is close to real situations.  
The University of Central Florida released the UCF-101 dataset [24] in 2012. The dataset samples include various action samples collected from TV stations and video samples saved from YouTube. There are 13,320 videos, including five types of actions (human-object interaction, human-human interaction, limb movements, body movement, and playing musical instruments), and 101 class-specific small actions.  
Brown University released the HMDB-51 dataset [25] in 2011. The samples come from video clips of YouTube. There are 51 types of sample actions and 6849 videos in total. Each type of sample action in the dataset contains at least 101 videos.  
The UCF-101 dataset and the HMDB-51 dataset have many action types and a wide range of actions. The scenes in the Hollywood2 dataset are more complex and closer to real life. To comprehensively verify the ST-D LSTM unit's performance, three datasets, UCF-10, HMDB-51, and Hollywood2, were chosen for training and testing. Furthermore, the ST-D LSTM unit's performance was tested in the above three databases, respectively. The UCF-101 and HMDB-51 datasets are commonly used in deep learning algorithms, so these two datasets were used when the ST-D LSTM unit was compared with other deep learning-based algorithms.