Scene space inference based on stereo vision

This paper provides an intuitive way to inference the space of a scene using stereo cameras. We first segmented the ground out of the image by adaptively learning the ground model in the image. We then used the convex hull to approximate the scene space. Objects within the scene can also be detected with the stereo cameras. Finally, we organized the scene space and the objects within the scene into a graphical model, and then used particle filters to approximate the solution. Experiments were conducted to test the accuracy of the ground segmentation and the precision and recall of object detection within the scene. The precision and recall of object detection was about 50% in our system. With additional tracking of the object, the recall could improve approximately 5%. The result can be considered as prior knowledge for further image tasks, e.g. obstacle avoidance or object recognition.