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Idling vehicles waste energy and pollute the environment through exhaustemission. In some countries, idling a vehicle for more than a predefinedduration is prohibited and automatic idling vehicle detection is desirable forlaw 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 andstopped cars and monitor the car temperature with a long-wavelength IR camera.We formulate the idling car detection problem as spatio-temporal eventdetection in IR image sequences and employ deep networks for spatio-temporalmodeling. We collected the first IR image sequence dataset for idling cardetection. First, we detect the cars in each IR image using a convolutionalneural network, which is pre-trained on regular RGB images and fine-tuned on IRimages for higher accuracy. Then, we track the detected cars over time toidentify the cars that are parked. Finally, we use the 3D spatio-temporal IRimage volume of each parked car as input to convolutional and recurrentnetworks to classify them as idling or not. We carried out an extensiveempirical evaluation of temporal and spatio-temporal modeling approaches withvarious convolutional and recurrent architectures. We present promisingexperimental results on our IR image sequence dataset.

Remote Detection of Idling Cars Using Infrared Imaging and Deep Networks