3D Multi Person Tracking With Dual 360° Cameras

Person tracking is an often studied facet of computer vision, with applications in security, automated driving and entertainment. However, despite the advantages they offer, few current solutions work for 360° cameras, due to projection distortion. This paper presents a simple yet robust method for 3D tracking of multiple people in a scene from a pair of 360° cameras. By using 2D pose information, rather than potentially unreliable 3D position or repeated colour information, we create a tracker that is both appearance independent as well as capable of operating at narrow baseline. Our results demonstrate state of the art performance on 360° scenes, as well as the capability to handle vertical axis rotation.

[1]  A. Hilton,et al.  3D Human Pose Estimation From Multi Person Stereo 360 Scenes , 2019, CVPR Workshops.

[2]  Kimiaki Shirahama,et al.  Unknown object tracking in 360-degree camera images , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[3]  Qiang Wang,et al.  Fast Online Object Tracking and Segmentation: A Unifying Approach , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Michael Felsberg,et al.  Accurate Scale Estimation for Robust Visual Tracking , 2014, BMVC.

[5]  Wei Wu,et al.  SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Stan Sclaroff,et al.  MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization , 2014, ECCV.

[7]  Mubarak Shah,et al.  Human Tracking in Multiple Cameras , 2001, ICCV.

[8]  Jianpeng Zhou,et al.  Real Time Robust Human Detection and Tracking System , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[9]  Luca Bertinetto,et al.  Fully-Convolutional Siamese Networks for Object Tracking , 2016, ECCV Workshops.

[10]  Alessandro Perina,et al.  Person re-identification by symmetry-driven accumulation of local features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Sridha Sridharan,et al.  Tracking by Prediction: A Deep Generative Model for Mutli-person Localisation and Tracking , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[12]  Hao Zhu,et al.  CrowdPose: Efficient Crowded Scenes Pose Estimation and a New Benchmark , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Harold W. Kuhn,et al.  The Hungarian method for the assignment problem , 1955, 50 Years of Integer Programming.

[14]  Bohyung Han,et al.  Learning Multi-domain Convolutional Neural Networks for Visual Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Mau-Tsuen Yang,et al.  Comparison of Tracking Techniques on 360-Degree Videos , 2019 .

[16]  Wei Wu,et al.  High Performance Visual Tracking with Siamese Region Proposal Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.