## 5 Discussion and Future Works
Such normalization is therefore relevant as it only changes the size of the image input and therefore does not change the network's architecture much while becoming better for the task it was designed for. Finally, Table 3, shows that by merging and fine-tuning both Geometric and Cartesian features branches into a single network, we can achieve better results for the C/NC task than by considering each branch independently. * **Inference time:** SPI-Net manages to preserve the information in a Euclidean grid space while keeping a coherent skeleton spatial structure and only uses 2D-convolutions and Dense layers in its architecture. It allows us to keep the advantages of convolutions in terms of parallel computing while capturing spatial and temporal features. Moreover, since SPI-Net only uses classic deep-learning operations, it could be easily implemented in any Deep Learning frameworks and also in any neural hardware solution like Intel Movidius(r), or FPGA without redefining any operations. For this study, we implement it by Keras [84] backend in Tensorflow [85]. Therefore, the knowledge of the optimization of euclidean data structure networks proposed in both libraries is conserved compared to approaches based on Graph Networks where basic operations need to be redefined and one might lose speed efficiency in the process. Since our SPI-Net approach has only \(\sim\)0.57 M parameters, its speed can reach around one inference every 0.25 ms on one GPU (i.e., RTX 2080 ti), or every 0.67 ms on one CPU (i.e., Intel Core i7 8700K), which is roughly 100 times faster than the current state-of-the-art Graph Convolutional Network [58] approach, the average speed of which takes 23 ms on two GPUs (i.e., two GTX 1080). Referring to human reaction times, visual skeletal representations are known to be sufficient for humans to describe and understand biological motion, specifically in the case of human motions [86] (i.e., walking, running). It comforts us in the idea of only working with skeleton-based models rather than image-based models. [PERSON] et al. [87] documented that the average reaction time to detect visual stimuli is approximately 180-200 ms: [PERSON] et al. [88] show that a visual stimulus takes around 20-40 ms to reach the brain, which leads to an average of 140-180 ms \"inference time\" for a human once the data reached the brain. SPI-Net relies on the Cascaded Pyramid Network (CPN) algorithm [14] to extract pose sequences before determining pedestrians' intentions. Therefore, one could argue that the pose extraction feature should be compared to the time to reach the brain information and SPI-Net should be compared to the average \"inference time\" for a human once the data reached the brain. Since the CPN took approximately 60 ms per frame to extract pedestrian poses, the overall approach is roughly two to three times faster than the average human reaction time to a stimulus. * **Image sampling and pose estimation method:** Necessary step of an intention prediction model of which the analysis of the posture is an essential component. One major drawback of our work is to rely on pose estimation algorithms. However, similarly to the OSI model, our approach relies on independent implementations of methods for specific tasks. It leads to a practical methodology: interchanging the pose estimation algorithms does not compromise the SPI-Net approach. Currently, one of the main limitations of a 2D pose estimation is the ability to deal with pedestrian occlusions in a two-dimensional space. Therefore, in order to improve the pose detection, the question of adding a third dimension may arise. Currently, the methods for estimating 3D poses are much less mature than those for 2D pose estimation. One of the main reasons, to this day, has been the lack of reliable data sets available [89]. However, our pipeline makes it easy to keep up with the state-of-the-art in this field without completely disrupting the SPI-Net approach for intention prediction.