Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data
A complete overview of the surrounding vehicle environment is important for driver assistance systems and highly autonomous driving. Fusing results of multiple sensor types like camera, radar and lidar is crucial for increasing the robustness. The detection and classification of objects like cars, bicycles or pedestrians has been analyzed in the past for many sensor types. Beyond that, it is also helpful to refine these classes and distinguish for example between different pedestrian types or activities. This task is usually performed on camera data, though recent developments are based on radar spectrograms. However, for most automotive radar systems, it is only possible to obtain radar targets instead of the original spectrograms. This work demonstrates that it is possible to estimate the body height of walking pedestrians using 2D radar targets. Furthermore, different pedestrian motion types are classified.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingGeneral ClassificationSimilar Papers 제목 키워드 기반
Predicting Future Pedestrian Motion in Video Sequences using Crowd Simulation
While human and group analysis have become an important area in last decades, some current and relevant applications involve to estimate future motion of pedestrians in real video sequences. This paper presents a method …
Motion EstimationSGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction
Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very challenging due to complex interactions between pedestrians. However, previous works based on dense undirected interaction suffe…
Pedestrian Trajectory PredictionPredictionTrajectory PredictionSGCN: Sparse Graph Convolution Network for Pedestrian Trajectory Prediction
Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very challenging due to complex interactions between pedestrians. However, previous works based on dense undirected interaction s…
Pedestrian Trajectory PredictionPredictionTrajectory PredictionMultiview Detection with Cardboard Human Modeling
Multiview detection uses multiple calibrated cameras with overlapping fields of views to locate occluded pedestrians. In this field, existing methods typically adopt a ``human modeling - aggregation'' strategy. To find r…
Depth EstimationMultiview DetectionLearning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction
Multi-pedestrian trajectory prediction is an indispensable element of autonomous systems that safely interact with crowds in unstructured environments. Many recent efforts in trajectory prediction algorithms have focused…
Pedestrian Trajectory PredictionPredictionTrajectory Prediction