Adaptive clustering for contour estimation of vehicles for high-resolution radar

Schlichenmaier, Johannes; Roos, Fabian; Kunert, Martin; Waldschmidt, Christian

doi:10.1109/icmim.2016.7533930

Cited by 27 publications

(7 citation statements)

References 6 publications

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“…Clustering algorithm is used to eliminate false alarm points and cluster the points of the same object into a class. The DBSCAN algorithm [31]- [34], known as one of the most efficient clustering algorithms for radar point clouds, is used in this paper. Using the DBSCAN algorithm, the points that are closely packed together are clustered and those lie in low-density regions are marked as outliers.…”

Section: ) Dbscan Algorithmmentioning

confidence: 99%

Point Cloud Features-Based Kernel SVM for Human-Vehicle Classification in Millimeter Wave Radar

et al. 2020

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Human-vehicle classification plays an important role in advanced driver assistance systems (ADAS). The use of millimeter wave (mmWave) radar sensor in human-vehicle classification algorithms is of great significance since the sensor maintains to be robust in severe weather (e.g. fog, snow, etc.). To improve classification accuracy under complex scenes of autonomous driving, a new mmWave radar point cloud classification algorithm is proposed in this paper, which realizes human-vehicle classification employing a newly proposed point cloud feature vector with eleven dimensions and based on kernel support vector machine (SVM) classifier. To verify the validity and robustness of the proposed feature vector, a 77 GHz radar is used to collect two datasets for static and moving objects, respectively, with each dataset taken for pedestrians and vehicles at different distances and angles. Experimental results show that the proposed algorithm achieves higher classification accuracy than a conventional one based on signal features. For the comparison based on the same number of dimensions, the number of dimensions of the proposed feature vector is decreased by removing the features with low significance. Experimental results verify that the proposed algorithm maintains advantage over the conventional one.

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Section: ) Dbscan Algorithmmentioning

confidence: 99%

Point Cloud Features-Based Kernel SVM for Human-Vehicle Classification in Millimeter Wave Radar

et al. 2020

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“…For this purpose, single measurements of two radars are collected and enhanced versions of orientated bounding box algorithms and the L-fit algorithm are applied. Schlichenmaier et al [12] show another approach to estimate bounding boxes using high-resolution radar data. For this reason, position and dimension of vehicles are estimated using a variant of the k-nearest-neighbors method.…”

Section: Related Workmentioning

confidence: 99%

2D Car Detection in Radar Data with PointNets

Danzer

Griebel

Bach

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

137

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For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to detect 2D objects solely depending on sparse radar data using PointNets. In literature, only methods are presented so far which perform either object classification or bounding box estimation for objects. In contrast, this method facilitates a classification together with a bounding box estimation of objects using a single radar sensor. To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal 2D bounding box. The algorithm is evaluated using an automatically created dataset which consist of various realistic driving maneuvers. The results show the great potential of object detection in high-resolution radar data using PointNets.

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“…To verify the effectiveness of the extended GHT approach, a total of 5,679 radar measurements were analyzed with the presented template based estimation (GHT/QF) and tested against a more classic approach using OBB, similar to [4].…”

Section: )mentioning

confidence: 99%

Template matching for radar-based orientation and position estimation in automotive scenarios

Schlichenmaier¹,

Selvaraj²,

Stolz

et al. 2017

2017 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)

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Adaptive clustering for contour estimation of vehicles for high-resolution radar

Cited by 27 publications

References 6 publications

Point Cloud Features-Based Kernel SVM for Human-Vehicle Classification in Millimeter Wave Radar

Point Cloud Features-Based Kernel SVM for Human-Vehicle Classification in Millimeter Wave Radar

2D Car Detection in Radar Data with PointNets

Template matching for radar-based orientation and position estimation in automotive scenarios

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