Optical flow estimation using the Fisher-Rao metric

被引:1
作者
Maybank, Stephen J. [1 ]
Ieng, Sio-Hoi [2 ]
Migliore, Davide [3 ]
Benosman, Ryad [2 ]
机构
[1] Birkbeck Coll, Dept Comp Sci & Informat Syst, Malet St, London WC1E 7HX, England
[2] Sorbonne Univ, Inst Vis, INSERM, CNRS, Paris, France
[3] Prophesee, 74 Rue Faubourg St Antoine, F-75012 Paris, France
来源
NEUROMORPHIC COMPUTING AND ENGINEERING | 2021年 / 1卷 / 02期
关键词
address event representation; AER; asynchronous image sensor; event camera; Fisher-Rao metric; Kullback-Leibler divergence; optical flow; DRIVEN;
D O I
10.1088/2634-4386/ac2bed
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The optical flow in an event camera is estimated using measurements in the address event representation (AER). Each measurement consists of a pixel address and the time at which a change in the pixel value equalled a given fixed threshold. The measurements in a small region of the pixel array and within a given window in time are approximated by a probability distribution defined on a finite set. The distributions obtained in this way form a three dimensional family parameterized by the pixel addresses and by time. Each parameter value has an associated Fisher-Rao matrix obtained from the Fisher-Rao metric for the parameterized family of distributions. The optical flow vector at a given pixel and at a given time is obtained from the eigenvector of the associated Fisher-Rao matrix with the least eigenvalue. The Fisher-Rao algorithm for estimating optical flow is tested on eight datasets, of which six have ground truth optical flow. It is shown that the Fisher-Rao algorithm performs well in comparison with two state of the art algorithms for estimating optical flow from AER measurements.
引用
收藏
页数:20
相关论文
共 40 条
[1]   What Can Neuromorphic Event-Driven Precise Timing Add to Spike-Based Pattern Recognition? [J].
Akolkar, Himanshu ;
Meyer, Cedric ;
Clady, Zavier ;
Marre, Olivier ;
Bartolozzi, Chiara ;
Panzeri, Stefano ;
Benosman, Ryad .
NEURAL COMPUTATION, 2015, 27 (03) :561-593
[2]  
Amari S.-I., 1985, DIFFERENTIAL GEOMETR, V28, DOI DOI 10.1007/978-1-4612-5056-2
[3]  
[Anonymous], 2017, OPTITRACK DOC WIK
[4]  
[Anonymous], 2015, Camera calibration toolbox for matlab
[5]   Mapping from Frame-Driven to Frame-Free Event-Driven Vision Systems by Low-Rate Rate Coding and Coincidence Processing-Application to Feedforward ConvNets [J].
Antonio Perez-Carrasco, Jose ;
Zhao, Bo ;
Serrano, Carmen ;
Acha, Begona ;
Serrano-Gotarredona, Teresa ;
Chen, Shouchun ;
Linares-Barranco, Bernabe .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2013, 35 (11) :2706-2719
[6]   Simultaneous Optical Flow and Intensity Estimation from an Event Camera [J].
Bardow, Patrick ;
Davison, Andrew J. ;
Leutenegger, Stefan .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :884-892
[7]   A Dataset for Visual Navigation with Neuromorphic Methods [J].
Barranco, Francisco ;
Fermuller, Cornelia ;
Aloimonos, Yiannis ;
Delbruck, Tobi .
FRONTIERS IN NEUROSCIENCE, 2016, 10
[8]   Contour Motion Estimation for Asynchronous Event-Driven Cameras [J].
Barranco, Francisco ;
Fermueller, Cornelia ;
Aloimonos, Yiannis .
PROCEEDINGS OF THE IEEE, 2014, 102 (10) :1537-1556
[9]   Event-Based Visual Flow [J].
Benosman, Ryad ;
Clercq, Charles ;
Lagorce, Xavier ;
Ieng, Sio-Hoi ;
Bartolozzi, Chiara .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 25 (02) :407-417
[10]   Asynchronous frameless event-based optical flow [J].
Benosman, Ryad ;
Ieng, Sio-Hoi ;
Clercq, Charles ;
Bartolozzi, Chiara ;
Srinivasan, Mandyam .
NEURAL NETWORKS, 2012, 27 :32-37