Remote detection of idling cars using infrared imaging and deep networks

Muhammet Bastan, Kim Hui Yap, Lap Pui Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Idling vehicles waste energy and pollute the environment through exhaust emission. In some countries, idling a vehicle for more than a predefined duration is prohibited and automatic idling vehicle detection is desirable for law enforcement. We propose the first automatic system to detect idling cars, using infrared (IR) imaging and deep networks. We rely on the differences 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 image sequences and employ deep networks for spatio-temporal modeling. We collected the first IR image sequence dataset for idling car detection. First, we detect the cars in each IR image using a convolutional neural network, which is pre-trained on regular RGB images and fine-tuned on IR images for higher accuracy. Then, we track the detected cars over time to identify the cars that are parked. Finally, we use the 3D spatio-temporal IR image volume of each parked car as input to convolutional and recurrent networks to classify them as idling or not. We carried out an extensive empirical evaluation of temporal and spatio-temporal modeling approaches with various convolutional and recurrent architectures. We present promising experimental results on our IR image sequence dataset.

Original languageEnglish
Pages (from-to)3047-3057
Number of pages11
JournalNeural Computing and Applications
Volume32
Issue number8
DOIs
Publication statusPublished - 1 Apr 2020
Externally publishedYes

Keywords

  • Car detection
  • Deep neural networks
  • Idle detection
  • Infrared image

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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