Dr. Muhammad Zeshan Afzal

Prof. Dr. Didier Stricker

Dr. Alain Pagani

Keonna Cunningham

Dr. Oliver Wasenmüller

Dr. Gabriele Bleser

Dr. Bruno Mirbach

Dr. Jason Raphael Rambach

Dr. Bertram Taetz

Mhd Rashed Al Koutayni

Jilliam Maria Diaz Barros

Iuliia Brishtel

Mahdi Chamseddine

Steve Dias da Cruz

Ahmet Firintepe

Sophie Folawiyo

David Michael Fürst

Kamalveerkaur Garewal

Christiano Couto Gava

Leif Eric Goebel

Tewodros Amberbir Habtegebrial

Jigyasa Singh Katrolia

Andreas Kölsch

Onorina Kovalenko

Muhammad Jameel Nawaz Malik

Nareg Minaskan Karabid

Mohammad Minouei

Manthan Pancholi

Engr. Kumail Raza

Dr. Nadia Robertini

María Alejandra Sánchez Marín

Dr. Kripasindhu Sarkar

Alexander Schäfer

Pascal Schneider

Lukas Stefan Staecker

Dr. Vladislav Golyanik

Dr. Aditya Tewari

André Luiz Brandão

3D Human Pose Tracking inside Car using Single RGB Spherical Camera

3D Human Pose Tracking inside Car using Single RGB Spherical Camera
Pramod Murthy, Onorina Kovalenko, Ahmed Elhayek, Christiano Couto Gava, Didier Stricker
ACM Chapters Computer Science in Cars Symposium (CSCS-17), July 6, Munich, Germany

Abstract:: The recent progress in Deep Learning methods in computer vision has resulted in improved Advanced Driver Assistance Systems (ADAS). The goal of ADAS is not only to assist drivers, but also to alert them before dangerous driving maneuvers. ADAS often do not capture human motions to augment it into the driving context. In this work, we attempt to propose a system to track 3D motion of humans (especially drivers) inside a car using single RGB spherical camera. We use a CNN based 3D human pose tracking system to track driver and passengers poses. Finally, we illustrate accurate results in real-time on recorded driving scenes.

Keywords:: 3D Human Pose Tracking, CNN, Human Activity, Deep Learning

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