Self lane assignment using egocentric smart mobile camera for intelligent GPS navigation

In this paper, we study the self lane assignment problem, i.e. given an image taken inside a vehicle, infer on which lane the image is taken. This problem serves as an example of active egocentric vision application with data fusion. In this application, a camera is mounted inside the vehicle looking outside to the world. Combined with a GPS with a digital map this smart mobile camera is capable of reasoning on which lane the vehicle is. This inference result is then fed back to the GPS to provide the driver with more intelligent navigation instructions. We form the self lane assignment inference problem as a scene classification problem which requires classifying scenes in finer categories than the traditional case. We design the features to represent the image in a holistic way bypassing individual object detection, develop an automatic horizon detection algorithm, and employ and compare three learning algorithms for decision making on the lane number. The experiment results show that our method can achieve the precision and recall rates around or above 90% at the same time.