## 2 Related Works
### Action Recognition Datasets  
In the literature there exist many datasets that provide a list of different actions depending on the recognition task for which they have been designed. Table 2 shows a list of the most used datasets for human action recognition based on RGB data. For each dataset we report a brief description, the total number of action samples and the number of classes. **IXMAS**[45]: the dataset contains 15 actions captured from different viewpoints. A total of 11 people perform the following actions: nothing, check watch, cross arms, scratch head, sit down, get up, turn around, walk, wave, punch, kick, point, pick up, throw (over head), throw (from bottom up). The acquisition device is a low-resolution 23 fps RGB camera. Its position is fixed, just as background, illumination and environment where actions are performed. **UCF Sport**[46]: the dataset is a collection of sport videos acquired from a wide range of video sources. It contains 150 video sequences belonging to 10 actions: diving, golf swing, kicking, lifting, riding horse, running, skateboarding, swing-bench, swing-side, and walking. **Hollywood 2**[47]: twelve frequent actions in movies are considered in this dataset: answer phone, drive car, eat, fight person, get out car, hand shake, hug person, kiss, run, sit down, sit up, stand up. These actions have been labelled from 69 movie scripts and the corresponding sequence included in the dataset for a total of 600,000 frames and 7 h of video. The dataset also contains scene labelling. **HMDB-51**[23]: the dataset contains 51 action categories for a total of about 7000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube videos. Each action category contains at least 101 clips. The actions comprise: facial actions (e.g., smile), facial actions involving objects (e.g., smoking), body movements (e.g., clap hands), body movements involving objects (e.g., draw sword), human interaction (e.g., fencing). **MSR Daily Activity 3D**[48]: contains 16 different activities acquired with a Kinect device: drink, eat, read book, call cellphone, write on a paper, use laptop, use vacuum cleaner, cheer up, sit still, toss paper, play game, lay down on sofa, walk, play guitar, stand up, sit down. If possible, the subject performs each activity in two different position sitting and standing. The acquisition environment is the same for all the sequences using a fixed camera position. **UCF-50**[49]: the dataset contains a set of 50 actions whose videos are taken from the web. The videos are characterized by random camera motion, poor lighting conditions, clutter, as well as changes in scale, appearance, and viewpoints. The actions in the dataset are very heterogeneous and some examples are tai chi, rowing, play piano, tossing balls, and biking. **UCF-101**[24]: it is an extension of the UCF-50 dataset. 51 new actions are added bringing the total action classes to 101 and the total 13,320 video clips. Each class has an average of 125 clips. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Year** & **Samples** & **Classes** \\ \hline IXMAS [45] & 2006 & 396 & 15 \\ UCF Sport [46] & 2008 & 150 & 10 \\ Hollywood 2 [47] & 2009 & 1707 & 12 \\ HMDB-51 [23] & 2011 & 6766 & 51 \\ MSR Daily Activity 3D [48] & 2012 & 320 & 16 \\ UCF-101 [24] & 2012 & 13,320 & 101 \\ UCF-50 [49] & 2013 & 6618 & 50 \\ N-UCLA [50] & 2014 & 1475 & 10 \\ Sports 1M [51] & 2014 & 1,133,158 & 487 \\ UWA3D II [52] & 2016 & 1075 & 30 \\ Kinetics [25] & 2017 & 306,245 & 400 \\ DALY [53] & 2016 & 3600 & 10 \\ Charades [26] & 2016 & 9848 & 157 \\ NTU [27] & 2016 & 56,880 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of the main RGB datasets used in human action recognition research field.