Patrick Gebert

Patrick Gebert

Karlsruhe, Baden-Württemberg, Deutschland
485 Follower:innen 459 Kontakte

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Info

I am a skilled Software Engineer with a passion for building and shipping high-quality…

Aktivitäten

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Berufserfahrung

  • ETECTURE GmbH Grafik

    ETECTURE GmbH

    Karlsruhe, Baden-Württemberg, Deutschland

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    Karlsruhe und Umgebung, Deutschland

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    Karlsruhe und Umgebung, Deutschland

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    Karlsruhe und Umgebung, Deutschland

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    Karlsruhe und Umgebung, Deutschland

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    Karlsruhe und Umgebung, Deutschland

Ausbildung

  • Karlsruher Institut für Technologie (KIT) Grafik

    Karlsruher Institut für Technologie (KIT)

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    Master of Science in Informatics at the Karlsruhe Institute of Technology (KIT) specialized in anthropomatics / cognitive systems and robotics / automation.

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Bescheinigungen und Zertifikate

Ehrenamt

Veröffentlichungen

  • End-to-end Prediction of Driver Intention using 3 D Convolutional Neural Networks

    Despite extraordinary progress of Advanced Driver Assistance Systems (ADAS), an alarming number of over 1,2 million people are still fatally injured in traffic accidents every year1. Human error is mostly responsible for such casualties, as by the time the ADAS system has alarmed the driver, it is often too late. We present a vision-based system based on deep neural networks with 3D convolutions and residual learning for anticipating the future maneuver based on driver observation. While…

    Despite extraordinary progress of Advanced Driver Assistance Systems (ADAS), an alarming number of over 1,2 million people are still fatally injured in traffic accidents every year1. Human error is mostly responsible for such casualties, as by the time the ADAS system has alarmed the driver, it is often too late. We present a vision-based system based on deep neural networks with 3D convolutions and residual learning for anticipating the future maneuver based on driver observation. While previous work focuses on hand-crafted features (e.g. head pose), our model predicts the intention directly from video in an end-to-end fashion. Our architecture consists of three components: a neural network for extraction of optical flow, a 3D residual network for maneuver classification and a Long Short-Term Memory network (LSTM) for handling temporal data of varying length. To evaluate our idea, we conduct thorough experiments on the publicly available Brain4Cars benchmark, which covers both inside and outside views for future maneuver anticipation. Our model is able to predict driver intention with an accuracy of 83,12% and 4,07s before the beginning of the maneuver, outperforming state-of-the-art approaches, while considering the inside view only.

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Auszeichnungen/Preise

  • 2. Place EnBW Hackathon

    EnBw Energie Baden-Württemberg AG , Karlsruhe, DE

    Computer Vision application for tracking people in urban environments.

  • 1. Place KIT Campus Hackathon

    Fakultät für Informatik - Karlsruher Institut für Technologie (KIT)

    Web application for collaborative food orders.

Sprachen

  • Französisch

    Grundkenntnisse

  • Englisch

    Gute Kenntnisse

  • Deutsch

    Muttersprache oder zweisprachig

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