Abstract
Deep learning has achieved great successes in performing many visual recognition tasks including object detection. Nevertheless, existing deep networks are computationally expensive and memory intensive, hindering their deployment in resource-constrained environments, such as mobile or embedded devices that are widely used by city travellers. Recently, estimating city-level travel patterns using street imagery has shown to be a potentially valid way according to a case study with Google Street View (GSV), addressing a critical challenge in transport object detection. This paper presents a compressed deep network using tensor decomposition to detect transport objects in GSV images, which is sustainable and eco-friendly. In particular, a new dataset named Transport Mode Share-Tokyo (TMS-Tokyo) is created to serve the public for transport object detection. This is based on the selection and filtering of 32,555 acquired images that involve 50,827 visible transport objects (including cars, pedestrians, buses, trucks, motors, vans, cyclists and parked bicycles) from the GSV imagery of Tokyo. Then a compressed convolutional neural network (termed SVDet) is proposed for street view object detection via tensor train decomposition on a given baseline detector. Experimental results conducted on the TMS-Tokyo dataset demonstrate that SVDet can achieve promising performance in comparison with conventional deep detection networks.
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Acknowledgements
This work is supported in part by the Strategic Partner Acceleration Award (80761-AU201), funded under the Ser Cymru II programme, UK. The first author is supported with a full International PhD Scholarship awarded by Aberystwyth University.
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Bai, Y., Shang, C., Li, Y., Shen, L., Zeng, X., Shen, Q. (2024). Transport Object Detection in Street View Imagery Using Decomposed Convolutional Neural Networks. In: Panoutsos, G., Mahfouf, M., Mihaylova, L.S. (eds) Advances in Computational Intelligence Systems. UKCI 2022. Advances in Intelligent Systems and Computing, vol 1454. Springer, Cham. https://doi.org/10.1007/978-3-031-55568-8_34
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