Comparison of random forest and long short-term memory network performances in classification tasks using radar

Wöhler, Christian; Schumann, Ole; Hahn, Markus; Dickmann, Jürgen

doi:10.1109/sdf.2017.8126350

Cited by 66 publications

(60 citation statements)

References 8 publications

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“…(14). As a consequence, the likelihoods are higher for the same detections than in the naive fusion (19). In contrast, the naive fusion reports the lowest probability at the beginning of the tracks.…”

Section: A Existence Probabilitymentioning

confidence: 87%

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Occlusion aware sensor fusion for early crossing pedestrian detection

Palffy

Kooij

Gavrila

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Early and accurate detection of crossing pedestrians is crucial in automated driving to execute emergency manoeuvres in time. This is a challenging task in urban scenarios however, where people are often occluded (not visible) behind objects, e.g. other parked vehicles. In this paper, an occlusion aware multi-modal sensor fusion system is proposed to address scenarios with crossing pedestrians behind parked vehicles. Our proposed method adjusts the detection rate in different areas based on sensor visibility. We argue that using this occlusion information can help to evaluate the measurements. Our experiments on real world data show that fusing radar and stereo camera for such tasks is beneficial, and that including occlusion into the model helps to detect pedestrians earlier and more accurately.

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Section: A Existence Probabilitymentioning

confidence: 87%

“…Several radar based pedestrian detection systems were described in the literature. A radar based multi-class classifier system (including pedestrian and group of pedestrians) was shown in [19]. [7] and [8] both aim to distinguish pedestrians from vehicles using features like size and velocity profiles of the objects using radar.…”

Section: Related Workmentioning

confidence: 99%

Occlusion aware sensor fusion for early crossing pedestrian detection

Palffy

Kooij

Gavrila

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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“…were compared in a single-class (pedestrian) detection task. [2] also uses clusters calculated by DBSCAN as the base of a multi-class (car, pedestrian, group of pedestrians, cyclist, truck) detection, but extract different features, e.g. deviation and spread of α.…”

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%

CNN Based Road User Detection Using the 3D Radar Cube

Palffy

Dong

Kooij

et al. 2020

IEEE Robot. Autom. Lett.

132

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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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“…High-level autonomous driving requires understanding of complex scenes. To this end [12] examined the classification of several dynamic object classes using random forests and long short-term memory (LSTM) cells. Both approaches yield good performance, but the class results have a bias towards overproportionally represented classes in the training set.…”

Section: Introductionmentioning

confidence: 99%

Radar-based Feature Design and Multiclass Classification for Road User Recognition

Scheiner

Appenrodt

Dickmann

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions. Radar sensors provide an -with respect to well established camera systems -orthogonal way of measuring such scenes. In order to gain accurate classification results, 50 different features are extracted from the measurement data and tested on their performance. From these features a suitable subset is chosen and passed to random forest and long short-term memory (LSTM) classifiers to obtain class predictions for the radar input. Moreover, it is shown why data imbalance is an inherent problem in automotive radar classification when the dataset is not sufficiently large. To overcome this issue, classifier binarization is used among other techniques in order to better account for underrepresented classes. A new method to couple the resulting probabilities is proposed and compared to others with great success. Final results show substantial improvements when compared to ordinary multiclass classification.

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Comparison of random forest and long short-term memory network performances in classification tasks using radar

Cited by 66 publications

References 8 publications

Occlusion aware sensor fusion for early crossing pedestrian detection

Occlusion aware sensor fusion for early crossing pedestrian detection

CNN Based Road User Detection Using the 3D Radar Cube

Radar-based Feature Design and Multiclass Classification for Road User Recognition

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