Pedestrian Classification with a 79 GHz Automotive Radar Sensor

Prophet, Robert; Hoffmann, Marcel; Vossiek, Martin; Sturm, Christian; Ossowska, Alicja; Malik, Waqas Ahmed; Lubbert, Urs

doi:10.23919/irs.2018.8448161

Cited by 24 publications

(31 citation statements)

References 6 publications

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“…The software of our pipeline is available on our website 1 . 1 https://github.com/tudelft-iv/RTCnet A. Pre-processing First, a single frame of radar targets and a single frame of the radar cube (low-level data) is fetched. Each radar target's speed is compensated for ego-motion similarly to [2].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Many road user detection methods start by clustering the radar targets into a set of object proposals. In [1], radar targets are first clustered into objects by DBSCAN [16]. Then, several cluster-wise features are extracted, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…E.g. both [1] and [2] have DBSCAN's minimum number of points to form a cluster (M inP oints) larger than one, which means that single standing points are thrown away.…”

Section: Related Workmentioning

confidence: 99%

“…[10] pointed out that a long multi-frame observation period is not viable for urban driving, and proposed a single-frame usage of low-level data. Their method still generates object proposals with DBSCAN similar to [1], [2], but extracts for each cluster the corresponding area in a 2D Range-Doppler image, which is then classified using conventional computer vision. In [23], the full radar cube is used as a multi-channel image input to a CNN network to classify cars, pedestrians, and cyclists.…”

Section: Related Workmentioning

confidence: 99%

“…Since a single reflection does not convey enough information to segment and classify an entire object, many radar based road user detection methods (e.g. [1], [2], [3]) first cluster radar targets by their target-level features. Clusters are then classified as a whole based on derived statistical features (e.g.…”

Section: Introductionmentioning

confidence: 99%

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CNN Based Road User Detection Using the 3D Radar Cube

Palffy

Dong

Kooij

et al. 2020

IEEE Robot. Autom. Lett.

140

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This paper presents a novel radar based, singleframe, multi-class detection method for moving road users (pedestrian, cyclist, car), which utilizes low-level radar cube data. The method provides class information both on the radar targetand object-level. Radar targets are classified individually after extending the target features with a cropped block of the 3D radar cube around their positions, thereby capturing the motion of moving parts in the local velocity distribution. A Convolutional Neural Network (CNN) is proposed for this classification step. Afterwards, object proposals are generated with a clustering step, which not only considers the radar targets' positions and velocities, but their calculated class scores as well. In experiments on a real-life dataset we demonstrate that our method outperforms the state-of-the-art methods both target-and objectwise by reaching an average of 0.70 (baseline: 0.68) target-wise and 0.56 (baseline: 0.48) object-wise F1 score. Furthermore, we examine the importance of the used features in an ablation study.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…E.g. both [1] and [2] have DBSCAN's minimum number of points to form a cluster (M inP oints) larger than one, which means that single standing points are thrown away.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

CNN Based Road User Detection Using the 3D Radar Cube

Palffy

Dong

Kooij

et al. 2020

IEEE Robot. Autom. Lett.

140

View full text Add to dashboard Cite

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Introduction to Radar Processing and Deep Learning

2022

Methods and Techniques in Deep Learning

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Semantic Segmentation on 3D Occupancy Grids for Automotive Radar

et al. 2020

Self Cite

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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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Pedestrian Classification with a 79 GHz Automotive Radar Sensor

Cited by 24 publications

References 6 publications

CNN Based Road User Detection Using the 3D Radar Cube

CNN Based Road User Detection Using the 3D Radar Cube

Introduction to Radar Processing and Deep Learning

Semantic Segmentation on 3D Occupancy Grids for Automotive Radar

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