Semantic Segmentation of Pedestrian Groups Based on Directional-oriented Density Features with Shallow U-net Architecture

Pedestrians typically form a small group with another pedestrian when they are traveling in the same direction and toward the same destination. While becoming members of a group of pedestrians, they dynamically interact with other pedestrians in the same group and outside the group. In order to understand pedestrian interaction, it is necessary to distinguish between pedestrians in a group and pedestrians not in a group. This can be achieved by performing semantic segmentation based on the JAAD dataset, which is suitable for observing pedestrian walking behavior with abundant annotation. In this research, we propose to perform semantic segmentation by utilizing directional-oriented density features. Density features are calculated by utilizing each joint relationship, while pedestrian direction can be predicted by calculated dot product based on shoulder, neck and hip joint. Segmentation is performed by employing a shallow U-network architecture, with fewer layers than the original U-network architecture. Compared with the original U-net architecture and its derived, our proposed method not only outperforms but also achieves stable performance from the early epoch in 6th epoch, and reaches a score over 0.97 during prediction, demonstrating its impressive performance.