This work presents a method for estimating human body orientation using a combination of convolutional neural network (CNN) and stereo camera in real time. The approach uses the CNN model to predict certain human body keypoints then transforms these points into a 3D space using the stereo vision system to estimate the body orientations. The CNN module is trained to estimate the shoulders, the neck and the nose positions, detecting of three points is required to confirm human detection and provided enough data to translate the points into 3D space.
Pedestrian Orientation Estimation Using CNN and Depth Camera
Sae Technical Papers
WCX SAE World Congress Experience ; 2020
2020-04-14
Conference paper
English
Pedestrian Orientation Estimation using On-board Monocular Camera with Semi-Supervised Learning
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