TY - GEN
T1 - Pooling-Based Feature Extraction and Coarse-to-fine Patch Matching for Optical Flow Estimation
AU - Tang, Xiaolin
AU - Phung, Son Lam
AU - Bouzerdoum, Abdesselam
AU - Tang, Van Ha
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - This paper presents a pooling-based hierarchical model to extract a dense matching set for optical flow estimation. The proposed model down-samples basic image features (gradient and colour) with min and max pooling, to maintain distinctive visual features from the original resolution to the highly down-sampled layers. Subsequently, patch descriptors are extracted from the pooling results for coarse-to-fine patch matching. In the matching process, the local optimum correspondence of patches is found with a four-step search, and then refined by a velocity propagation algorithm. This paper also presents a method to detect matching outliers by checking the consistency of motion-based and colour-based segmentation. We evaluate the proposed method on two benchmarks, MPI-Sintel and Kitti-2015, using two criteria: the matching accuracy and the accuracy of the resulting optical flow estimation. The results indicate that the proposed method is more efficient, produces more matches than the existing algorithms, and improves significantly the accuracy of optical flow estimation.
AB - This paper presents a pooling-based hierarchical model to extract a dense matching set for optical flow estimation. The proposed model down-samples basic image features (gradient and colour) with min and max pooling, to maintain distinctive visual features from the original resolution to the highly down-sampled layers. Subsequently, patch descriptors are extracted from the pooling results for coarse-to-fine patch matching. In the matching process, the local optimum correspondence of patches is found with a four-step search, and then refined by a velocity propagation algorithm. This paper also presents a method to detect matching outliers by checking the consistency of motion-based and colour-based segmentation. We evaluate the proposed method on two benchmarks, MPI-Sintel and Kitti-2015, using two criteria: the matching accuracy and the accuracy of the resulting optical flow estimation. The results indicate that the proposed method is more efficient, produces more matches than the existing algorithms, and improves significantly the accuracy of optical flow estimation.
KW - Coarse-to-fine patch matching
KW - Optical flow estimation
KW - Pooling-based feature extraction
UR - https://www.scopus.com/pages/publications/85066857853
U2 - 10.1007/978-3-030-20870-7_37
DO - 10.1007/978-3-030-20870-7_37
M3 - Conference contribution
AN - SCOPUS:85066857853
SN - 9783030208691
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 597
EP - 612
BT - Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Mori, Greg
A2 - Jawahar, C.V.
A2 - Schindler, Konrad
A2 - Li, Hongdong
PB - Springer Verlag
T2 - 14th Asian Conference on Computer Vision, ACCV 2018
Y2 - 2 December 2018 through 6 December 2018
ER -