Look and listen:A multi-modality late fusion approach to scene classification for autonomous machines


The novelty of this study consists in a multi-modality approach to scene classification, where image and audio complement each other in a process of deep late fusion. The approach is demonstrated on a difficult classification problem, consisting of two synchronised and balanced datasets of 16, 000 data objects, encompassing 4.4 hours of video of 8 environments with varying degrees of similarity. We first extract video frames and accompanying audio at one second intervals. The image and the audio datasets are first classified independently, using a fine-tuned VGG16 and an evolutionary optimised deep neural network, with accuracies of 89.27% and 93.72%, respectively. This is followed by late fusion of the two neural networks to enable a higher order function, leading to accuracy of 96.81% in this multi-modality classifier with synchronised video frames and audio clips. The tertiary neural network implemented for late fusion outperforms classical state-of-the-art classifiers by around 3% when the two primary networks are considered as feature generators. We show that situations where a single-modality may be confused by anomalous data points are now corrected through an emerging higher order integration. Prominent examples include a water feature in a city misclassified as a river by the audio classifier alone and a densely crowded street misclassified as a forest by the image classifier alone. Both are examples which are correctly classified by our multi-modality approach.

Publication DOI: https://doi.org/10.1109/IROS45743.2020.9341557
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Institute of Urban Technology and the Environment (ASTUTE)
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Event Title: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
Event Type: Other
Event Dates: 2020-10-24 - 2021-01-24
Uncontrolled Keywords: Image analysis,Neural networks,Urban areas,Forestry,Generators,Intelligent robots,Rivers,Control and Systems Engineering,Software,Computer Vision and Pattern Recognition,Computer Science Applications
ISBN: 978-1-7281-6213-3, 9781728162126
Last Modified: 07 May 2024 07:24
Date Deposited: 17 Sep 2021 08:34
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://ieeexpl ... ocument/9341557 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2021-02-10
Accepted Date: 2020-06-20
Authors: Bird, Jordan J. (ORCID Profile 0000-0002-9858-1231)
Faria, Diego R. (ORCID Profile 0000-0002-2771-1713)
Premebida, Cristiano
Ekart, Aniko (ORCID Profile 0000-0001-6967-5397)
Vogiatzis, George (ORCID Profile 0000-0002-3226-0603)



Version: Accepted Version

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