Multi-feature walking pedestrians detection for driving assistance systems

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Multi-feature walking pedestrians detection for driving assistance systems

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© The Institution of Engineering and Technology
Published

Pedestrians are the most vulnerable urban traffic participants. In order to better protect them in pre-crash scenarios, it is necessary to detect them. Unfortunately, pedestrian detection is very difficult in highly cluttered urban scenarios, using cameras mounted on moving vehicle. A novel approach to walking pedestrian detection, using dense stereo vision systems, is presented. Multiple features combined into a Bayesian framework are used to yield a high rate of pedestrian detection. The feature set includes simple features such as width, height, lateral and longitudinal speed. It also includes complex motion features, such as the variance of the motion field caused by the pedestrians' legs and arms moving during walking and the periodicity of the pedestrians' walking pattern.

Inspec keywords: stereo image processing; driver information systems; road traffic; feature extraction; road accidents; image sensors; Bayes methods; image classification; image motion analysis; object detection; road safety

Other keywords: driving assistance systems; Bayesian classification; highly cluttered urban scenarios; pedestrian walking pattern; urban traffic; complex motion features; moving cameras; motion analysis; dense stereo vision systems; multifeature walking pedestrian detection; pre-crash scenarios

Subjects: Other topics in statistics; Computer vision and image processing techniques; Optical, image and video signal processing; Traffic engineering computing; Other topics in statistics; Image sensors

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