# Human Action Recognition Based on Improved Two-Stream Convolution Network
## 4 Experiments
### Data Set  
In the field of action recognition, there are many data sets that are used. These include UCF101 and UCF50, HMDB, KTH, Hollywood, Hollywood 2, Kinetics and more.  
KTH data set [58], released in 2004, is one of the earliest published action recognition data sets. The data set includes 6 types of actions in 4 scenes, which are completed by 25 people, and the total number of videos is 2391. This data set is a milestone in the field of computer vision and has been widely used in the field of traditional action recognition by manually extracting features. However, due to its single background, few scenes, fixed perspective, and other restrictive conditions, it has not been widely used in recent years.  
The Hollywood [59] and Hollywood2 [60] data sets are successively released by the IRISA Research Institute of France in 2008 and 2009. As its name suggests, the Hollywood dataset comes from the films and television works produced by Hollywood. The Hollywood2 dataset contains 12 action categories in 10 scenes, and the number of videos is 3669 in total. The feature of the data set of film and television works is that people's actions, expressions, and gestures are relatively rich, and the background factors, such as the movement of viewing angle and lighting conditions are full of changes. Therefore, it is very challenging for the action recognition algorithm. However, there are still a few action categories.  
The HMDB data set [61] is a data set released by Brown University in 2011. The data set contains a rich number of scenes, including some from many public data sets and some from online video databases, such as YouTube. The data set contains 51 action categories, with a total of 6849 videos. Compared with the KTH and Hollywood data sets, the HMDB data set has greatly improved the number of video types and includes network video data. Its complexity and diversity lay a foundation for improving the generalization performance of action recognition algorithms.  
The UCF50 data set [62] and UCF101 data set [63] were published by Florida Central University in 2012 and 2013 respectively. The UCF101 data set contains 101 action categories, and the number of videos has reached 13,320. The data set widely contains video data from radio and television channels and the Internet. It is one of the data sets with the largest number of action categories at present. Among them, the amount of equal scale data and high video quality provide help for the application of action recognition algorithms with deep learning as a means of feature extraction.  
The Kinetics data set [64] was released in 2017. The dataset has multiple series sets, such as Kinetics400, Kinetics600, and Kinetics700, including 400, 600, and 700 action categories respectively. The number of videos has reached 500,000. This almost increases the number of action categories in multiple existing datasets by one order of magnitude. The data set is born for deep learning, and its massive video data is conducive to thetraining of the neural network. Unfortunately, not all devices can make full use of this data set, and the high hardware conditions limit the wide use of this data set.  
Considering the hardware conditions of experimental equipment and neural network structure, the UCF101 data set and HMDB51 data set are used in our experiments.