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Sensor planning chiefly applies to optimizing surveillance tasks, such as persistent tracking by designing and utilizing camera placement strategies. Against substituting new optimized camera networks for those still in usage, online sensor planning hereby involves the design of vision algorithms that not only select cameras which yield the best results, but also improve the quality with which surveillance tasks are performed. In previous literatures about coverage problem in sensor planning, targets (e.g., persons) are justly simplified as a 2-D point. However in actual application scene, cameras are always heterogeneous such as fixed with different height and action radii, and the monitored objects has 3-D features (e.g., height). This paper presents a new sensor planning formulation addressing the efficiency enhancement of active visual tracking in camera networks that track and detect people traversing a region. The numerical results show that this online sensor planning approach can improve the active tracking performance of the system.