Remote Detection of Idling Cars Using Infrared Imaging and Deep Networks

TL;DR

This paper introduces a novel infrared imaging and deep learning-based system for automatic detection of idling cars, aiming to reduce energy waste and pollution, with promising results on a newly collected dataset.

Contribution

It is the first system to detect idling cars using IR imaging and deep networks, combining spatio-temporal modeling and a new dataset for this task.

Findings

Effective IR-based detection of idling cars demonstrated

Deep networks outperform traditional methods in this task

System shows promising accuracy on the new dataset

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.