  We present an unsupervised adaptation approach for visual scene understanding
in unstructured traffic environments. Our method is designed for unstructured
real-world scenarios with dense and heterogeneous traffic consisting of cars,
trucks, two-and three-wheelers, and pedestrians. We describe a new semantic
segmentation technique based on unsupervised domain adaptation (DA), that can
identify the class or category of each region in RGB images or videos. We also
present a novel self-training algorithm (Alt-Inc) for multi-source DA that
improves the accuracy. Our overall approach is a deep learning-based technique
and consists of an unsupervised neural network that achieves 87.18% accuracy on
the challenging India Driving Dataset. Our method works well on roads that may
not be well-marked or may include dirt, unidentifiable debris, potholes, etc. A
key aspect of our approach is that it can also identify objects that are
encountered by the model for the fist time during the testing phase. We compare
our method against the state-of-the-art methods and show an improvement of
5.17% - 42.9%. Furthermore, we also conduct user studies that qualitatively
validate the improvements in visual scene understanding of unstructured driving
environments.
