Object direction estimation by Constrained Convolutional Neural Network
Jin Liu, Chuxuan Luo, Fang Gao · 2020
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.