To solve this problem, this paper proposed an improved multi-scale dense crowd detection method based on YOLOv5 and improved the DBSCAN clustering algorithm to identify densely crowded areas.

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of data clustering and anomaly detection using the DBSCAN (Density Based Spatial Clustering of Applications ...

Abstract: DBSCAN is the most famous density based clustering algorithm which is one of the main clustering paradigms. However, there are many redundant distance computations among the process of ...

In incremental approach, the DBSCAN algorithm is applied to a dynamic database where the data may be frequently updated. After insertions or deletions to the dynamic database, the clustering ...

The authors present, a new Parallel DBSCAN algorithm (PDSDBSCAN) using graph algorithmic concepts. More specifically, they employ the disjoint-set data structure to break the access sequentiality ...