  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.
