  Reliable markerless motion tracking of people participating in a complex
group activity from multiple moving cameras is challenging due to frequent
occlusions, strong viewpoint and appearance variations, and asynchronous video
streams. To solve this problem, reliable association of the same person across
distant viewpoints and temporal instances is essential. We present a
self-supervised framework to adapt a generic person appearance descriptor to
the unlabeled videos by exploiting motion tracking, mutual exclusion
constraints, and multi-view geometry. The adapted discriminative descriptor is
used in a tracking-by-clustering formulation. We validate the effectiveness of
our descriptor learning on WILDTRACK [14] and three new complex social scenes
captured by multiple cameras with up to 60 people "in the wild". We report
significant improvement in association accuracy (up to 18%) and stable and
coherent 3D human skeleton tracking (5 to 10 times) over the baseline. Using
the reconstructed 3D skeletons, we cut the input videos into a multi-angle
video where the image of a specified person is shown from the best visible
front-facing camera. Our algorithm detects inter-human occlusion to determine
the camera switching moment while still maintaining the flow of the action
well.
