# Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition
## 4 Experiments  
### Datasets  
NTU-RGB-D 60: NTU-RGB-D [45] is currently the most widely used action recognition dataset. It consists of 56,880 video clips in a total of 60 classes of actions performed by 40 subjects aged 10 to 35 years old. Each video clip contains an action, and each clip has 4 modalities: RGB video, depth map sequence, 3D skeleton data and infrared video. The data is captured by three Microsoft Kinect v.2 cameras at the same time, and the cameras are set at three different horizontal angles: \(-45^{\circ},0^{\circ}\) and \(45^{\circ}\). We used the 3D skeleton data in the experiment, which was obtained through the skeleton tracking technology in the Kinect camera. It is composed of the 3D position coordinates \((x,y,z)\) of the 25 main body joints of the human body, and there are at most two people in each clip sample. Two benchmarks are recommended in the dataset: cross-subject (X-sub) and cross-view (X-view). (1) For the cross-subject setting, the data is divided into training set and validation set, which contain 40320 video clips and 16,560 video clips, respectively. The subjects in the two sets are different, and 40 subjects are divided into a training group and a test group, each group containing 20 subjects. (2) For the cross-view setting, we select 18960 video clips captured by camera view 1 at \(-45^{\circ}\) for testing, 37,920 clips captured by camera views 2 and 3 at \(0^{\circ}\) and \(45^{\circ}\) for training. There are 302 video samples that need to be ignored. Following the convention in [45], we report the Top-1 accuracy on both benchmarks to evaluate recognition performance. NTU-RGB-D 120: NTU-RGB+D 120 [46] is an extended version of the NTU-RGB+D dataset. It consists of 114480 video clips in a total of 120 classes of actions performed by 106 distinct subjects from 15 different countries, and each subject has a consistent ID number. The author of the dataset recommends two benchmarks: cross-subject(X-sub) and cross-setup(X-setup). (1) For the cross-subject setting, 106 subjects are divided into training group and testing group (including 63,026 and 50,922 clips respectively), half of which are used for training and the other half are used for testing. (2) For the cross-setup setting, the training data comes from 54,471 video clips with even setup ID numbers, and the testing data comes from 59,477 clips with the remaining odd setup ID numbers. Among them, 532 bad video samples should be ignored. Following the convention in [46], we report the Top-1 accuracy on both benchmarks. Kinetics: Kinetics [47] is a large-scale video dataset for human action recognition, which contains about 300,000 video clips collected from YouTube; each clip is about 10 s. There are 400 action classes in the dataset, and there are at least 400 videos for each action. Since Kinetics only contains the original video clips, [PERSON] et al. [13] used the OpenPose toolbox to extract the skeleton data of the original data and estimated the position of each person's 18 joints on each frame. The tool can generate 2D coordinates \((x,y)\) and confidence scores \(c\) from the video clip, and we use \((x,y,c)\) to represent the joint. We selected the two people with the highest joint confidence if a clip contained multiple people. The dataset provides 240,000 clips as the training set and 20,000 clips as the validation set. We trained the model on the training set, and we used the Top-1 accuracy rate and Top-5 accuracy rate  
Figure 3: The overall architecture of our multi-scale adaptive aggregate graph convolutional network. It consists of 9 basic modules (as shown in Figure 1), which are indicated by dark-colored circles, and the numbers below these circles indicate their input and output channels, respectively. Each module contains the residual links in the figure. Light-colored BN circle represents batch normalization layer for data normalization, and the standard Softmax classifier is used for action category. to evaluate the model on the validation set.