Object direction estimation by Constrained Convolutional Neural Network

Jin Liu; Chuxuan Luo; Fang Gao

doi:10.1117/12.2541936

14 February 2020 Object direction estimation by Constrained Convolutional Neural Network

Jin Liu, Chuxuan Luo, Fang Gao

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 1143024 (2020) https://doi.org/10.1117/12.2541936
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

We propose Constrained Convolutional Neural Network, a novel approach to estimate the direction of numerous target objects. Considering adding a constrained layer at the output of existing object detection networks, by which CCNN performs better in both accuracy and speed than previous neural networks as it works with filtered data, and obtains a more precise result. In object direction estimation, by means of constraint structures, forward and backward propagation algorithms redesigned for the quaternions which describe the 3D pose of the object, CCNN can be further applied to 3D pose estimation. Experiments show that CCNN is feasible for object direction detection and 3D pose estimation, and outperforms conventional neural networks without unitized constrained layer.

Citation Download Citation

Jin Liu, Chuxuan Luo, and Fang Gao "Object direction estimation by Constrained Convolutional Neural Network", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143024 (14 February 2020); https://doi.org/10.1117/12.2541936

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available