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2021 | OriginalPaper | Buchkapitel

Fine-Tuning for One-Look Regression Vehicle Counting in Low-Shot Aerial Datasets

verfasst von : Aneesh Rangnekar, Yi Yao, Matthew Hoffman, Ajay Divakaran

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

We investigate the task of entity counting in overhead imagery from the perspective of re-purposing representations learned from ground imagery, e.g., ImageNet, via feature adaptation. We explore two directions of feature adaptation and analyze their performances using two popular aerial datasets for vehicle counting: PUCPR+ and CARPK. First, we explore proxy self-supervision tasks such as RotNet, jigsaw, and image inpainting to re-fine the pretrained representation. Second, we insert additional network layers to adaptively select suitable features (e.g., squeeze and excitation blocks) or impose desired properties (e.g., using active rotating filters for rotation invariance). Our experimental results show that different adaptations produce different amounts of performance improvements depending on data characteristics. Overall, we achieve a mean absolute error (MAE) of 3.71 and 5.93 on the PUCPR+ and CARPK datasets, respectively, outperforming the previous state of the art: MAEs of 5.24 for PUCPR+ and 7.48 for CARPK.

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Nächstes Kapitel Generative Data Augmentation for Vehicle Detection in Aerial Images
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Metadaten
Titel
Fine-Tuning for One-Look Regression Vehicle Counting in Low-Shot Aerial Datasets
verfasst von
Aneesh Rangnekar
Yi Yao
Matthew Hoffman
Ajay Divakaran
Copyright-Jahr
2021
Buch
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68792-2

Electronic ISBN: 978-3-030-68793-9

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-68793-9_1