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

A Graph Transduction Game for Multi-target Tracking