Pedestrian Orientation Estimation Using CNN and Depth Camera

Abughalieh, Karam; Alawneh, Shadi

doi:10.4271/2020-01-0700

Cited by 4 publications

(2 citation statements)

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“…We were able to estimate the body orientation using these points by building vectors using the shoulders points, and another vector using hips points. In another work [4], a CNN model was developed to detect the body landmarks of the shoulders, neck and face only, this time the points were translated to a 3D space using a depth camera. Then the same concept of using vectors to compute the orientation is applied successfully.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed approach in this work builds on the ideas from a previous work [4] presenting an enhanced CNN model for body landmarks detection in addition to a pedestrian intention to cross the street detector, the detector is based on detecting sudden changes of pedestrian orientation toward the street. The novel contributions of this research work can be summarized in the following:…”

Section: Introductionmentioning

confidence: 99%

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Predicting Pedestrian Intention to Cross the Road

Abughalieh

Alawneh

2020

IEEE Access

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The goal of this research is the development of a driver assistant feature, which can warn the driver in case a pedestrian is in a potential risk due to sudden intention to cross the road. The process of crossing pedestrian is defined as the changing of pedestrian orientation on the curb toward the road. We built a Convolutional Neural Network (CNN) model combined with depth sensing camera to estimate the pedestrian orientation and distance from the vehicle. The model detects the higher human body keypoints in 2D space while the depth info make it possible to translate the points into a 3D space. These info are tracked per pedestrian and any change in the pedestrian moving pattern toward the road is translated to a warning for the driver. The CNN model is end-end trained using different datasets presenting pedestrian in different configurations and scenes.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%