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We present an unsupervised adaptation approach for visual scene understandingin unstructured traffic environments. Our method is designed for unstructuredreal-world scenarios with dense and heterogeneous traffic consisting of cars,trucks, two-and three-wheelers, and pedestrians. We describe a new semanticsegmentation technique based on unsupervised domain adaptation (DA), that canidentify the class or category of each region in RGB images or videos. We alsopresent a novel self-training algorithm (Alt-Inc) for multi-source DA thatimproves the accuracy. Our overall approach is a deep learning-based techniqueand consists of an unsupervised neural network that achieves 87.18% accuracy onthe challenging India Driving Dataset. Our method works well on roads that maynot be well-marked or may include dirt, unidentifiable debris, potholes, etc. Akey aspect of our approach is that it can also identify objects that areencountered by the model for the fist time during the testing phase. We compareour method against the state-of-the-art methods and show an improvement of5.17% - 42.9%. Furthermore, we also conduct user studies that qualitativelyvalidate the improvements in visual scene understanding of unstructured drivingenvironments.

BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments