Idling Car Detection with ConvNets in Infrared Image Sequences

We propose a system to detect and localize idling cars in infrared (IR) image sequences for law enforcement to reduce vehicular emission. To this end, we leverage the differences in spatio-temporal heat signatures of idling and stopped cars and monitor car temperatures with a long-wavelength IR camera. We collected a dataset by recording IR image sequences of cars in car parks and trained a ConvNet-based car detector to localize stationary cars in the IR sequences, by utilizing transfer learning and models pre-trained on regular RGB/grayscale images. Then, we used ConvNets with a 3D stack of cropped frames as input to model the spatio-temporal evolution of car temperature over time and detect idling cars. We present promising experimental results on our IR image dataset.

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