Name
Title | ||
|---|---|---|
Homography Estimation Along Short Videos By Recurrent Convolutional Regression Network |
Abstract | ||
|---|---|---|
Many moving-camera video processing and analysis tasks require accurate estimation of homography across frames. Estimating homography between non-adjacent frames can be very challenging when their camera view angles show large difference. In this paper, we propose a new deep-learning based method for homography estimation along videos by exploiting temporal dynamics across frames. More specifically, we develop a recurrent convolutional regression network consisting of convolutional neural network (CNN) and recurrent neural network (RNN) with long short-term memory (LSTM) cells,followed by a regression layer for estimating the parameters of homography. In the experiments, we evaluate the proposed method on both the synthesized and real-world short videos. The experimental results verify that the proposed method can estimate the homographies along short videos better than several existing methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3934/mfc.2020014 | MATHEMATICAL FOUNDATIONS OF COMPUTING |
Keywords | DocType | Volume |
Homography, short videos, recurrent convolutional network, regression | Journal | 3 |
Issue | Citations | PageRank |
2 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Mi | 1 | 67 | 16.04 |
| Kang Zheng | 2 | 42 | 7.41 |
| Song Wang | 3 | 954 | 79.55 |