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This paper focuses on the compacted probabilistic binary visual classification for human targets in highly constrained wireless multimedia sensor network (WMSN). With consideration of robustness and accuracy, Gaussian process classifier (GPC) is used for classifier learning, since it can provide a Bayesian framework to automatically determine the optimal or near optimal kernel hyper-parameters. For decreasing computing complexity, feature compaction are carried out before learning, which are implemented by integer lifting wavelet transform (ILWT) and rough set. Then, the individual decisions of multiple nodes are combined by committee decision for improving the robustness and accuracy. Experimental results verify that GPC with committee decision can effectively carry out binary human target classification in WMSN. Importantly, GPC outperforms support vector machine, especially when committee decision is used. Furthermore, ILWT and rough set can offer compact representation of effective features, which can decrease the learning time and increase the learning accuracy.