402 citations · 614 across the 7 of their papers we have counts for
7 papers
3D Radar and Camera Co-Calibration: A Flexible and Accurate Method for Target-based Extrinsic Calibration
Lei Cheng, Arindam Sengupta, Siyang Cao
Advances in autonomous driving are inseparable from sensor fusion. Heterogeneous sensors are widely used for sensor fusion due to their complementary properties, with radar and cam…
mmPose-NLP: A Natural Language Processing Approach to Precise Skeletal Pose Estimation using mmWave Radars
Arindam Sengupta, Siyang Cao
In this paper we presented mmPose-NLP, a novel Natural Language Processing (NLP) inspired Sequence-to-Sequence (Seq2Seq) skeletal key-point estimator using millimeter-wave (mmWave)…
mmFall: Fall Detection using 4D MmWave Radar and a Hybrid Variational RNN AutoEncoder
Feng Jin, Arindam Sengupta, Siyang Cao
In this paper we propose mmFall - a novel fall detection system, which comprises of (i) the emerging millimeter-wave (mmWave) radar sensor to collect the human body's point cloud a…
mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs
Arindam Sengupta, Feng Jin, Renyuan Zhang +1
In this paper, mm-Pose, a novel approach to detect and track human skeletons in real-time using an mmWave radar, is proposed. To the best of the authors' knowledge, this is the fir…
Automotive Radar Interference Mitigation Using Adaptive Noise Canceller
Feng Jin, Siyang Cao
Interference among frequency modulated continues wave automotive radars can either increase the noise floor, which occurs in the most cases, or generate a ghost target in rare situ…
Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs
Feng Jin, Renyuan Zhang, Arindam Sengupta +4
To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which…
MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring
Feng Jin, Arindam Sengupta, Siyang Cao +1
In multimodal traffic monitoring, we gather traffic statistics for distinct transportation modes, such as pedestrians, cars and bicycles, in order to analyze and improve people's d…