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Wireless cameras can be used to gather situation awareness information (e.g., humans in distress) in disaster recovery scenarios. However, blindly sending raw video streams from such cameras, to an operations center or controller can be prohibitive in terms of bandwidth. Further, these raw streams could contain either redundant or irrelevant information. Thus, we ask “how do we extract accurate situation awareness information from such camera nodes and send it in a timely manner, back to the operations center?” Towards this, we design ACTION, a framework that (a) detects objects of interest (e.g., humans) from the video streams, (b) combines these streams intelligently to eliminate redundancies and (c) transmits only parts of the feeds that are sufficient in achieving a desired detection accuracy to the controller. ACTION uses small amounts of metadata to determine if the objects from different camera feeds are the same. A resource-aware greedy algorithm is used to select a subset of video feeds that are associated with the same object, so as to provide a desired accuracy, for being sent to the operations center. Our evaluations show that ACTION helps reduce the network usage up to threefold, and yet achieves a high detection accuracy of ≈ 90%.