# Grassmann Manifold Based State Analysis Method of Traffic Surveillance Video
## 4 Experiment Results and Discussion
### Analysis of Traffic Video Status  
(1) Analysis of the UCSD Dataset: We put each video into Grassmann manifold by using auto-regressive moving-average (ARMA) model. ARMA model is a common model which is used to describe stationary random sequence. This model includes three forms: (1) Auto-regressive (AR) model. (2) Moving-average (MA) model. (3) ARMA model. The observing size is set to 5 and the dimensions of the subspace are set to 10 in ARMA model. We compare our method with GDA, GGDA, KAHM, LDS, CS-LDS and GSR, the results shown in Table 2.  
The videos in the dataset are from the same camera, and the dataset contains different weather and light conditions. Through Table 2, we can see that the accuracy of our method (GNN) is pretty good, and the method gets the highest accuracy. (2) Analysis of Capital Airport Expressway Dataset: After preparing the dataset and thinking through the experimental design work, we begin to conduct the experiment. First of all, we divide the traffic states into three levels, \"heavy\",\"medium\" and \"light\". Then we extract features by Grassmann manifold and combine these features with sparse representation and neural network. Finally, we classify the dataset by using methods of machine learning, where 20 percent of the data are selected for training and 80 percent of the data are selected for testing. To verify the effect of our proposed method in a variety of situations, this Expressway Dataset is divided into five datasets, and each dataset contains different numbers of videos and different conditions. We select two datasets named Data-video-original and Data-video-expand, which respectively include 120 and 270 videos, and both of the datasets are just videos in daytime. The traffic video status classification accuracy of these two datasets is shown in Table 3.  
After taking into consideration that the actual situation is not only daytime, but also contains a lot of night time (bad light conditions), we expand the datasets with Data-vi \begin{table}
\begin{tabular}{c c c c} \hline
**Name** & **Number of Video** & **Including Daytime Video** & **Including Night Video** \\ \hline Data-video-original & 120 & yes & no \\ Data-video-expand & 270 & yes & no \\ Data-video-darklight & 300 & yes & yes \\ Data-video-darklight-only & 300 & no & yes \\ Data-video-mix & 900 & yes & yes \\ \hline \end{tabular}
\end{table} 
Table 1: Information of the dataset. \begin{table}
\begin{tabular}{c c} \hline
**Methods** & **Accuracy (\%)** \\ \hline GDA ([PERSON] 2008) & 79.1 \\ GGDA ([PERSON] et al. 2011) & 89.4 \\ KARM ([PERSON] and [PERSON] 2010) & 82.7 \\ LDS ([PERSON] et al.2010) & 87.8 \\ CS-LDS ([PERSON] et al. 2010) & 86.6 \\ GSR (Grassmann Sparse Representation) & 82.8 \\ GNN (Grassmann Neural Networks) & 99.5 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of the experiment. \begin{table}
\begin{tabular}{c c} \hline
**Dataset** & **Data-Video-Original (\%)** & **Data-Video-Expand (\%)** \\ \hline GGDA & 45.24 & 82.54 \\ GSR & 95.80 & 99.50 \\ GNN & 98.96 & 99.54 \\ \hline \end{tabular}
\end{table} 
Table 3: The accuracy of these two datasets. Data-video-darklight-only dataset and Data-video-mix dataset. The traffic video status classification accuracy of the three datasets is shown in Table 4.  
There are both daytime and nighttime TSVs in the dataset, and the use of headlight is inconsistent, so the light conditions are complicated. On the other hand, obviously, the weather is not always the same, ranging from sunny, cloudy, foggy and so on. All the reasons above show that it is especially difficult to get high accuracy using this dataset. Although there are many difficulties, considering the inner relation through the front-neighbor camera and behind-neighbor camera, our method (GNN) achieves the desired effect. According to Tables 3 and 4, there is no doubt that our methodology acquired higher classification accuracy among all the methods. In the experiment, we used the UCSD dataset and built Capital Airport Expressway dataset.