Low-data-rate wireless networks can be deployed for physical intrusion detection and localization purposes. The intrusion of a physical object (or human) will disrupt the radio-frequency magnetic field and can be detected by observing the change in radio attenuation. This gives the basis for the radio tomographic imaging technology, which has recently been developed for passively monitoring and tracking objects. Due to noise and the lack of knowledge about the number and the sizes of intruding objects, multiobject intrusion detection and localization is a challenging issue. This paper proposes an extended variational Bayesian Gaussian mixture model (VB-GMM) algorithm in treating this problem. The extended VB-GMM algorithm applies a Gaussian mixture model to model the changed radio attenuation in a monitored field due to the intrusion of an unknown number of objects and uses a modified version of the variational Bayesian approach for model estimation. Real-world data from both outdoor and indoor experiments (using the radio tomographic imaging technology) have been used to verify the high accuracy and the robustness of the proposed multiobject localization algorithm.