# Human Action Recognition Based on 3D Convolution and Multi-Attention Transformer
## 5 Experimental Results and Analyses  
### Datasets  
We evaluate our approach on three widely used action recognition datasets: UCF101 [30], HMDB51 [31], and KTH [32]. To reduce the training difficulty of the model and enhance its performance, we employ a pretrained model on the Kinetics-400 [33] dataset.  
**UCF101**: The UCF101 dataset is an expanded version of the UCF50 dataset, comprising over 13,000 videos sourced from YouTube and organized into 101 distinct action classes. This dataset offers a diverse range of actions captured from various angles, featuring complexities such as object appearance, varied viewpoints, camera motion, cluttered backgrounds, and different lighting conditions. The videos within each action class are divided into 25 groups, with each group containing 4 to 7 videos. The action classes can be broadly categorized into five types: human-object interaction, human-human interaction, body motion, sports, and playing musical instruments. The UCF101 dataset is partitioned into a training set with approximately 9500 videos and a test set with around 3700 videos.  
**HMDB51**: The HMDB51 dataset comprises over 6000 videos, primarily sourced from internet movies. These videos are categorized into 51 action classes, with most actions representing everyday activities. Each action class contains at least 101 videos. The action classes can be broadly divided into two main categories: facial actions and body movements. All videos in the HMDB51 dataset are annotated with detailed information, including action class labels, video conditions, and metadata. The annotations specify the position of the body, the visibility of the body parts, and the number of objects involved in the actions. The HMDB51 dataset is divided into a training set with approximately 3500 videos and a test set with around 1500 videos.  
**KTH**: The KTH action dataset, released by the Royal Institute of Technology (KTH) in Sweden in 2004, is a widely used video dataset for action recognition research. The dataset consists of 600 video sequences, each annotated with specific action categories and performer information. It includes six basic action categories, with each action performed by 25 different individuals across four distinct scenarios: outdoor, outdoor with scale variation, outdoor with different clothing, and indoor.  
**Kinetics-400**: The Kinetics-400 dataset is a large-scale and meticulously labeled video dataset, encompassing 400 diverse action classes. It comprises 240,000 training videos, 40,000 testing videos, and 20,000 validation videos. Each action class contains over 600 video instances. The dataset contains a wide range of actions, including human-object interactions such as riding a bike and typing, as well as human-human interactions like shaking hands and salsa dancing.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Integration Approach** & **Recognition Accuracy (ACC)/\%** \\ \hline Seek peace and integration & 87.6 \\ Average fusion & 84.3 \\ Maximum fusion & 85.5 \\ Weighted integration & 88.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy of different fusion methods on UCF101.