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Open Access A combined HOG and deep convolution network cascade for pedestrian detection

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For the analysis of the interaction patterns of traffic participants, a robust visual detector and tracker for pedestrians and vehicles has been developed. The resulting implementation is currently being used to analyze hundreds of hours of recorded videos. This work concentrates on the detector for pedestrians, which combines several key concepts into a processing framework, which can run close to real-time even without GPU acceleration: a fast and efficient HOG detector cascade is combined with a deep convolutional network to combine the advantages of both algorithms. In addition to the detector, this work covers also aspects of camera calibration, which is used to control the scale of detection windows based on the viewing geometry.

The evaluation of our detector on the CALTECH database as well as on real world ground truth videos and manually annotated sample data demonstrates the effectiveness of our approach.
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Document Type: Research Article

Publication date: January 29, 2017

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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