CONVOLUTIONAL NEURAL NETWORK FOR CAMERA POSE ESTIMATION FROM OBJECT DETECTIONS

Known scene geometry and camera calibration parameters give important information to video content analysis systems. In this paper, we propose a novel method for camera pose estimation based on people observation in the input video captured by static camera. As opposed to previous techniques, our me...

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Bibliographic Details
Main Authors: Shalnov, E. V., Konushin, A. S.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Cameras
Content analysis
Data
Mathematical models
Methods
Neural networks
Pose estimation
Online Access:Get full text
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Description
Summary:Known scene geometry and camera calibration parameters give important information to video content analysis systems. In this paper, we propose a novel method for camera pose estimation based on people observation in the input video captured by static camera. As opposed to previous techniques, our method can deal with false positive detections and inaccurate localization results. Specifically, the proposed method does not make any assumption about the utilized object detector and takes it as a parameter. Moreover, we do not require a huge labeled dataset of real data and train on the synthetic data only. We apply the proposed technique for camera pose estimation based on head observations. Our experiments show that the algorithm trained on the synthetic dataset generalizes to real data and is robust to false positive detections.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLII-2-W4-1-2017