Clustering of Closely Adjacent Extended Objects in Radar Images using Velocity Profile Analysis

Schlichenmaier, Johannes; Roos, Fabian; Hügler, Philipp; Waldschmidt, Christian

doi:10.1109/icmim.2019.8726765

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…With only radar data of a single frame, Kellner et al [16,17] compute full velocity of moving vehicles from radial velocities and azimuth angles of at least two radar hits. However, for a robust solution, the method requires that 1) radar captures more radar hits on each object, 2) radar points have significantly different azimuth angles and 3) object points are clustered before velocity estimation [16,32,31]. Obviously due to sparsity of radar in a single frame, it is difficult to obtain at least two radar hits on distant vehicles, let alone objects of smaller sizes.…”

Section: Velocity Estimation In Perception Systemsmentioning

confidence: 99%

Full-Velocity Radar Returns by Radar-Camera Fusion

Long

Morris

Liu

et al. 2021

Preprint

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A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points. However, the missing tangential velocity component hampers object velocity estimation as well as temporal integration of radar sweeps in dynamic scenes. Recognizing that fusing camera with radar provides complementary information to radar, in this paper we present a closed-form solution for the point-wise, full-velocity estimate of Doppler returns using the corresponding optical flow from camera images. Additionally, we address the association problem between radar returns and camera images with a neural network that is trained to estimate radar-camera correspondences. Experimental results on the nuScenes dataset verify the validity of the method and show significant improvements over the state-of-the-art in velocity estimation and accumulation of radar points.

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Section: Velocity Estimation In Perception Systemsmentioning

confidence: 99%

Full-Velocity Radar Returns by Radar-Camera Fusion

Long

Morris

Liu

et al. 2021

Preprint

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show abstract

“…Multiple recent publications have dealt with improvements in object-based, dynamic environment models. This mainly includes the grouping of radar targets to the corresponding object [1], the estimation of object geometry and state of motion [2], and finally their assignment to an object class, such as pedestrian or car [3]- [5]. For fully autonomous driving, however, the creation of a static environment model is also indispensable, for example, to identify the road course ahead according to the infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation on 3D Occupancy Grids for Automotive Radar

et al. 2020

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Radar sensors have great advantages over other sensors in estimating the motion states of moving objects, because they detect velocity components within one measurement cycle. Moreover, numerous successes have already been achieved regarding the classification of such objects. However, the advantage of instantaneous velocity measurement is lost when detecting static objects, so that their classification is much more demanding. In this paper, we use semantic segmentation networks to distinguish between frequently occurring infrastructure objects. The resulting semantic grids provide a location-based classification of the vehicle environment. Since even modern radars have a significantly poorer angular resolution than lidars, the relatively thin radar point cloud is accumulated in advance and transformed into 2D or 3D grids that act as network inputs. Occupancy grids are particularly advantageous here, since they calculate not only the obstacles but also the free spaces. With suitable parameter selection, which is very challenging due to the complexity of radar measurement, the resulting grids allow for good association with camera images. Finally, in order to evaluate possible advantages of 3D grids as network input with respect to the segmentation result, we created and evaluated a simulation dataset and two different real-world datasets in car parks and on motorways. As a result, Jaccard coefficients between 81% and 88% were achieved, depending on the dataset. It was also found that 3D input images lead to improvements in the car park dataset.

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“…Eryildirim and Guldogan proposed a method to track a single extended target based on Bernoulli filter [22], [23]. Schlichenmaier et al identified two closely adjacent extended targets based on the analysis of target velocity profile [24]. By knowing the number of extended target, Lan and Li presented an approach for classifying extended object using random matrix and target size provided [25].…”

Section: Introductionmentioning

confidence: 99%

An Applied Method for Clustering Extended Targets With UHF Radar

et al. 2020

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In this paper, the application of coherent ultra-high frequency (UHF) Doppler radar for ship target detection over river is investigated. Due to the wide beam and high resolution of UHF radar, ship target echoes are usually significantly extended in both the range and Doppler dimensions of the radar Range-Doppler (R-D) spectrum. The range and radial velocity of the extended target are difficult to be determined using a constant false alarm rate (CFAR) detector, especially for the low-radial-velocity case in which the detection performance of CFAR detector is deteriorated due to strong river clutter. To solve this problem, an applied clustering method is proposed to detect and classify multiple targets and obtain corresponding target centers from the CFAR outputs. The target extension characteristics, which are used for clustering, are modeled and employed in segments for different range. The effectiveness of the proposed method is validated using both simulated and field data and the clustering method can classify extended targets without the need of knowing the number of targets beforehand.INDEX TERMS UHF radar, extended target, target detection, constant false alarm rate (CFAR), clustering method.

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Clustering of Closely Adjacent Extended Objects in Radar Images using Velocity Profile Analysis

Cited by 8 publications

References 15 publications

Full-Velocity Radar Returns by Radar-Camera Fusion

Full-Velocity Radar Returns by Radar-Camera Fusion

Semantic Segmentation on 3D Occupancy Grids for Automotive Radar

An Applied Method for Clustering Extended Targets With UHF Radar

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