Hide and Seek an active binocular object tracking system

We introduce a novel active stereo vison-based object tracking system for a humanoid robot. The system tracks a moving object that is dynamically changing its appearance and scale. The system features an in-built learning process that simultaneously learns short term models for the object and potential distractors. These models evolve over time, rectifying the inaccuracies of the tracking in a cluttered scene and allowing the system to identify unusual events such as sudden displacement, hiding behind or being masked by an occluder, and sudden disappearance from the scene. The system deals with these through different response modes such as active search when the object is lost, intentional waiting for reappearance when the object is hidden, and reinitialization of the track when the object is suddenly displaced by the user. We demonstrate our system on the iCub robot in an indoor environment and evaluate its performance. Our experiments show a performance enhancement for long occlusions through the learning of distractor models.