Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow - Evaluation Dataset

Mapping an ever changing urban environment is a challenging task as we are generally interested in mapping the static scene and not the dynamic objects therein, such as cars and people. We propose a novel approach to the problem of dynamic object removal within stereo based scene mapping that is both independent of the underlying stereo approach in
use and applicable to varying object motion relative to scene depth and camera motion. By leveraging both stereo odometry to recover camera motion in scene space and our stereo disparity to recover synthesised optic flow over the same in pixel space we isolate regions of inconsistency in depth an image intensity. This allows us to illustrate robust dynamic object removal within the stereo mapping sequence. We show results covering objects with a range of motion dynamics and sizes of those typically observed in an urban environment using this evaluation dataset.

Used in the paper: Generalized Dynamic Object Removal for Dense Stereo Vision Based Scene Mapping using Synthesised Optical Flow (O.K. Hamilton, T.P. Breckon), In Proc. International Conference on Image Processing, IEEE, 2016.