Advanced Radar Micro-Doppler Simulation Environment for Human Motion Applications

Ishak, Karim; Appenrodt, Nils; Dickmann, Jürgen; Waldschmidt, Christian

doi:10.1109/radar.2019.8835755

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…In [138], radar measurements are enriched with GNSS ground-truth for ML-based VRU recognition. Furthermore, synthesizing VRU radar responses with radar simulators has been presented with the motion ground-truth obtained from kinematic models [139], animations [140], [141], or Kinect data [142], [143].…”

Section: E Machine Learning and Automotive Radarmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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Section: E Machine Learning and Automotive Radarmentioning

confidence: 99%

Automotive Radar — From First Efforts to Future Systems

2021

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“…The authors of [14] published source code, which is only suitable for static scenes. Furthermore, we managed to reproduce the method explained in [16] for exporting moving pointtargets from Blender using "speed vector pass". However, the point-target information was accurate enough only for 2D movements but not for complex 3D motions.…”

Section: Distinction From Related Workmentioning

confidence: 99%

“…It is worth pointing out that the graphics in the scene were static and only the Radar sensor was allowed to move. In [16], the authors overcame this problem by using another rendered image called "speed vector pass", which contains information about pixels moving in two dimensions (x and y). The authors were able to generate spectrogram from a synthetically generated human figure that was waving both hands.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Radar Dataset Generator for Macro-Gesture Recognition

et al. 2021

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Recent developments in mmWave technology allow the detection and classification of dynamic arm gestures. However, achieving a high accuracy and generalization requires a lot of samples for the training of a machine learning model. Furthermore, in order to capture variability in the gesture class, the participation of many subjects and the conduct of many gestures with different arm speed are required. In case of macro-gestures, the position of the subject must also vary inside the field of view of the device. This would require a significant amount of time and effort, which needs to be repeated in case that the sensor hardware or the modulation parameters are modified. In order to reduce the required manual effort, here we developed a synthetic data generator that is capable of simulating seven arm gestures by utilizing Blender, an open-source 3D creation suite. We used it to generate 600 artificial samples with varying speed of execution and relative position of the simulated subject, and used them to train a machine learning model. We tested the model using a real dataset recorded from ten subjects, using an experimental sensor. The test set yielded 84.2% accuracy, indicating that synthetic data generation can significantly contribute in the pre-training of a model.

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“…Approaches can be distinguished with respect to the underlying human target model, which can be based on a set of char- [12] GAN - [13] GAN - [14] Simulation MoCap [16], [17] Simulation Kinect [15], [21] Simulation Kinect [18], [19] Simulation Blender [20] Simulation Blender [23] Domain Transfer Video (Mono) This work Simulation Video (Stereo) 1 DA: direct augmentation, 2 PT: pre-training, 3 CD: cross-domain training acteristic skeletal keypoints, whose positions are derived from motion capture data [14] or Kinect sensors [15], [16], [17], and models that derive scattering centers from 3D body models generated e.g. with help of computer graphics software [18], [19], [20]. Starting from these simulation approaches, some papers recently explored the potential of simulated radar data for augmentation.…”

Section: Introductionmentioning

confidence: 99%