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A Continuous Occlusion Model for Road Scene Understanding
CVPR 2016 | We present a physically interpretable 3D model for handling occlusions with applications to road scene understanding. Given object detection and SFM point tracks, our unified model probabilistically assigns point tracks to objects and reasons about object detection scores and bounding boxes. It uniformly handles static and dynamic objects, thus outperforming motion segmentation for association problems. It also demonstrates occlusion-aware 3D localization in road scenes.
Collaborators: Vikas Dhiman, Quoc-Huy Tran, Jason Corso, Manmohan Chandraker
