Constraint-Based Hierarchical Cluster Selection in Automotive Radar Data
High-resolution automotive radar sensors play an increasing role in detection, classification and tracking of moving objects in traffic scenes. Clustering is frequently used to group detection points in this context. However, this is a particularly challenging task due to variations in number and de...
Main Authors: | Claudia Malzer, Marcus Baum |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-05-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/10/3410 |
Similar Items
-
Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach
by: Lianhui Wang, et al.
Published: (2021-05-01) -
Adaptive Regularized Semi-Supervised Clustering Ensemble
by: Rui Luo, et al.
Published: (2020-01-01) -
Semi-Supervised Density Peaks Clustering Based on Constraint Projection
by: Shan Yan, et al.
Published: (2020-11-01) -
IDENTIFYING WAREHOUSE LOCATION USING HIERARCHICAL CLUSTERING
by: Sebastjan ŠKERLIČ, et al.
Published: (2016-09-01) -
Clustering with Instance and Attribute Level Side Information
by: Jinlong Wang, et al.
Published: (2010-12-01)