This paper proposes a novel online domain-shift appearance learning and object tracking scheme on a Riemannian manifold for visual and infrared videos, especially for video scenarios containing large deformable objects with fast out-of-plane pose changes that could be accompanied by partial occlusions. Although Riemannian manifolds and covariance descriptors are promising for visual object trac...

Autonomous systems that operate in dynamic environments require robust object tracking 3D as one of their key components. Most recent approaches for multi-object (MOT) from LIDAR use dynamics together with a set handcrafted features to match detections objects across multiple frames. However, manually designing such and heuristics is cumbersome often leads suboptimal performance. In this work, ...