  Semi-supervised learning is a popular class of techniques to learn from
labeled and unlabeled data. The paper proposes an application of a recently
proposed approach of graph transduction that exploits game theoretic notions to
the problem of multiple people tracking. Within the proposed framework, targets
are considered as players of a multi-player non-cooperative game. The
equilibria of the game is considered as a consistent labeling solution and thus
an estimation of the target association in the sequence of frames. Patches of
persons are extracted from the video frames using a HOG based detector and
their similarity is modeled using distances among their covariance matrices.
The solution we propose achieves satisfactory results on video surveillance
datasets. The experiments show the robustness of the method even with a heavy
unbalance between the number of labeled and unlabeled input patches.
