  In this paper, a deep domain adaptation based method for video smoke
detection is proposed to extract a powerful feature representation of smoke.
Due to the smoke image samples limited in scale and diversity for deep CNN
training, we systematically produced adequate synthetic smoke images with a
wide variation in the smoke shape, background and lighting conditions.
Considering that the appearance gap (dataset bias) between synthetic and real
smoke images degrades significantly the performance of the trained model on the
test set composed fully of real images, we build deep architectures based on
domain adaptation to confuse the distributions of features extracted from
synthetic and real smoke images. This approach expands the domain-invariant
feature space for smoke image samples. With their approximate feature
distribution off non-smoke images, the recognition rate of the trained model is
improved significantly compared to the model trained directly on mixed dataset
of synthetic and real images. Experimentally, several deep architectures with
different design choices are applied to the smoke detector. The ultimate
framework can get a satisfactory result on the test set. We believe that our
approach is a start in the direction of utilizing deep neural networks enhanced
with synthetic smoke images for video smoke detection.
