## 5 Discussion and Future Works  
* **Ablation studies:** From Table 1, we figure that solely using the Geometric features branch alone cannot produce a satisfactory performance for the C/NC task: since most of the prior to crossing actions are strongly correlated to global spatial motion of the pedestrian in the scene, the usage of only relative Euclidean distances between key-points is missing necessary information such as spatial dynamics or sequential modeling. However, the Geometric features branch still seems to capture some information only relative to the orientation and dynamics of the skeleton in the data without explicit temporal modeling or global spatial information. For this study, it was of interest to investigate if using the data projected into the latent space provided more information compared to the initial Geometric features input without fine-tuning the entire approach and updating the weights of the network. Table 1 shows that, by using the same binary classifier on the projected data in the bottleneck obtained from a classical auto-encoder, a simple LDA finds slightly more meaning in the data than the initial Geometric features input. Moreover, the latent space representation obtained by our regularized auto-encoder seems to be a little bit more informative than a regular auto-encoder latent space representation. In Figure 8, we evaluate the correspondence between the value of \(\lambda\) for the supervised separability constraint part and prediction accuracy. Afterward, we evaluate the necessity of using a pre-trained encoder network for classification initialized with an auto-encoder training. By comparing the results from the same network with [PERSON]'s weights initialization [64] prior to any auto-encoder training to the entire geometric branch approach, we show that using an auto-encoder to initialize the network's \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Geometric Branch**} & \multicolumn{2}{c}{**Cartesian Branch**} & \multicolumn{2}{c}{**SPI-Net**} \\ \hline
**Ground Truth** & **Crossing** & **Not Crossing** & **Crossing** & **Not Crossing** & **Crossing** & **Not Crossing** \\ \hline Crossing & 37 & 25 & 57 & 5 & 60 & 2 \\ \hline Not Crossing & 16 & 46 & 9 & 53 & 5 & 57 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix of the JAAD data set obtained by each branch of SPI-Net and SPI-Net on JAAD for the C/NC task. \begin{table}
\begin{tabular}{c c} \hline
**Method** & **Accuracy** \\ \hline Alexnet + Context [15] & 63.0\% \\ \hline Alexnet + SVM [50] & 74.4\% \\ \hline Alphabose + LSTM [56] & 78.0\% \\ \hline Res-EnDec [53] & 81.0\% \\ \hline ST-DenseNet [52] & 84.76\% \\ \hline auto-encoder + Prediction[54] & 86.7\% \\ \hline Openpose + Keypoints [55] & 88.0\% \\ \hline Alexnet + SVM + Context [50] & 89.4\% \\ \hline CPN + GCN [58] & 91.9\% \\ \hline
**CPN + Geometric branch (\(\lambda=5\))** & 69.4\% \\ \hline
**CPN + Cartesian branch** & 88.7\% \\ \hline
**CPN + SPI-Net (\(\lambda=5\))** & **94.4\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracies for pedestrian intention prediction for the C/NC task in JAAD. CPN [14], Alphabose [82] and Openpose [83] stand for the use of human pose estimation algorithms used by the skeleton-based features method. We have also included the results reported in [15; 50], where CNN features are based on a non-fine-tuned AlexNet [51] and Context refers to features of the environment, not of the pedestrian itself. weights helps to a certain extent the network's accuracy. From Table 2, we can deduce that by taking into consideration both spatial and temporal features in the Cartesian coordinate system, we obtain better results than by only considering relative distances of given key-points of the pedestrian skeleton. We can also conclude that the usage of the Tree Structure Skeleton Image (TSSI) [76] normalization improves the results of the Cartesian branch for the C/NC task considerably.