Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144919
Title: Remote detection of idling cars using infrared imaging and deep networks
Authors: Bastan, Muhammet
Yap, Kim-Hui
Chau, Lap-Pui
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Bastan, M., Yap, K.-H., & Chau, L.-P. (2020). Remote detection of idling cars using infrared imaging and deep networks. Neural Computing and Applications, 32(8), 3047-3057. doi:10.1007/s00521-019-04077-0
Journal: Neural Computing and Applications
Abstract: Idling vehicles waste energy and pollute the environment through exhaust emission. In some countries, idling a vehicle formore than a predefined duration is prohibited and automatic idling vehicle detection is desirable for law enforcement. Wepropose the first automatic system to detect idling cars, using infrared (IR) imaging and deep networks. We rely on thedifferences in spatio-temporal heat signatures of idling and stopped cars and monitor the car temperature with a long-wavelength IR camera. We formulate the idling car detection problem as spatio-temporal event detection in IR imagesequences and employ deep networks for spatio-temporal modeling. We collected the first IR image sequence dataset foridling car detection. First, we detect the cars in each IR image using a convolutional neural network, which is pre-trainedon regular RGB images and fine-tuned on IR images for higher accuracy. Then, we track the detected cars over time toidentify the cars that are parked. Finally, we use the 3D spatio-temporal IR image volume of each parked car as input toconvolutional and recurrent networks to classify them as idling or not. We carried out an extensive empirical evaluation oftemporal and spatio-temporal modeling approaches with various convolutional and recurrent architectures. We presentpromising experimental results on our IR image sequence dataset.
URI: https://hdl.handle.net/10356/144919
ISSN: 0941-0643
DOI: 10.1007/s00521-019-04077-0
Rights: © 2019 Springer-Verlag London Limited. This is a post-peer-review, pre-copyedit version of an article published in Neural Computing and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00521-019-04077-0
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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