Search Results for author: Feng Jin

Found 6 papers, 2 papers with code

mmFall: Fall Detection using 4D MmWave Radar and a Hybrid Variational RNN AutoEncoder

1 code implementation5 Mar 2020 Feng Jin, Arindam Sengupta, Siyang Cao

Moreover, to circumvent the difficulties in fall data collection/labeling, the VRAE is built upon an autoencoder architecture in a semi-supervised approach, and trained on only normal activities of daily living (ADL) such that in the inference stage the VRAE will generate a spike in the anomaly level once an abnormal motion, such as fall, occurs.

Variational Inference

mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs

no code implementations21 Nov 2019 Arindam Sengupta, Feng Jin, Renyuan Zhang, Siyang Cao

To the best of the authors' knowledge, this is the first method to detect >15 distinct skeletal joints using mmWave radar reflection signals.

Autonomous Vehicles Decision Making

Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs

no code implementations14 Nov 2019 Feng Jin, Renyuan Zhang, Arindam Sengupta, Siyang Cao, Salim Hariri, Nimit K. Agarwal, Sumit K. Agarwal

For each patient, the Doppler pattern of the point cloud over a time period is collected as the behavior signature.

MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring

1 code implementation14 Nov 2019 Feng Jin, Arindam Sengupta, Siyang Cao, Yao-Jan Wu

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 daily mobility in terms of safety and convenience.

General Classification Point Cloud Segmentation

Neural Network Multitask Learning for Traffic Flow Forecasting

no code implementations24 Dec 2017 Feng Jin, Shiliang Sun

Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks.

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