GDSCAN: Pedestrian Group Detection using Dynamic Epsilon

Abstract

In order to maintain human safety in autonomous vehicles, pedestrian detection and tracking in real-time have become crucial research areas. The critical challenge in this field is to improve pedestrian detection accuracy while reducing tracking processing time. Due to the fact that pedestrians move in groups with the same speed and direction, we can address this challenge by detecting and tracking pedestrian groups. This work focused on pedestrian group detection. Various clustering methods were used in this study to identify pedestrian groups. Firstly, pedestrians were identified using a convolutional neural network approach. Secondly, K-Means and DBSCAN clustering methods were used to identify pedestrian groups based on the coordinates of the pedestrians’ bounding boxes. Moreover, we proposed a modified DBSCAN clustering method named GDSCAN that employs dynamic epsilon to different areas of an image. The experimental results on the MOT17 dataset show that GDSCAN outperformed K-Means and DBSCAN methods based on the Silhouette Coefficient score and Adjusted Rand Index (ARI).