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AuthorAhishali, Mete
AuthorYamac, Mehmet
AuthorKiranyaz, Serkan
AuthorGabbouj, Moncef
Available date2023-09-24T08:57:18Z
Publication Date2023
Publication NameNeural Networks
ResourceScopus
ISSN0893-6080
URIhttp://dx.doi.org/10.1016/j.neunet.2022.11.011
URIhttp://hdl.handle.net/10576/47888
AbstractIn this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional Support Estimator Networks (CSENs). CSENs are designed to compute a direct mapping for the Support Estimation (SE) task in a representation-based classification problem. We further propose and demonstrate that representation-based methods (sparse or collaborative representation) can be used in well-designed regression problems especially over scarce data. To the best of our knowledge, this is the first representation-based method proposed for performing a regression task by utilizing the modified CSENs; and hence, we name this novel approach as Representation-based Regression (RbR). The initial version of CSENs has a proxy mapping stage (i.e., a coarse estimation for the support set) that is required for the input. In this study, we improve the CSEN model by proposing Compressive Learning CSEN (CL-CSEN) that has the ability to jointly optimize the so-called proxy mapping stage along with convolutional layers. The experimental evaluations using the KITTI 3D Object Detection distance estimation dataset show that the proposed method can achieve a significantly improved distance estimation performance over all competing methods. Finally, the software implementations of the methods are publicly shared at https://github.com/meteahishali/CSENDistance.
SponsorThis work was supported by the NSF-Business Finland Center for Visual and Decision Informatics (CVDI) under the project Advanced Machine Learning for Industrial Applications (AMaLIA).
Languageen
PublisherElsevier
SubjectConvolutional support estimator network
Object distance estimation
Representation-based regression
Sparse support estimation
TitleRepresentation based regression for object distance estimation
TypeArticle
Pagination15-29
Volume Number158
dc.accessType Open Access


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