Ground penetrating radar (GPR) is a popular sensing modality for buried threat detection that offers low false alarm rates (FARs), but suffers from a short detection stopping or standoff distance. This short stopping distance leaves little time for the system operator to react when a threat is detected, limiting the speed of advance. This problem arises, in part, because of the way GPR data is typically processed. GPR data is first prescreened to reduce the volume of data considered for higher level feature-processing. Although fast, prescreening introduces latency that delays the feature processing and lowers the stopping distance of the system. In this work we propose a novel sensor fusion framework where a forward looking infrared (FLIR) camera is used as a prescreener, providing suspicious locations to the GPRbased system with zero latency. The FLIR camera is another detection modality that typically yields a higher FAR than GPR while offering much larger stopping distances. This makes it well-suited in the role of a zero-latency prescreener. In this framework, GPR-based feature processing can begin without any latency, improving stopping distances. This framework was evaluated using well-known FLIR and GPR detection algorithms on a large dataset collected at a Western US test site. Experiments were conducted to investigate the tradeoff between early stopping distance and FAR. The results indicate that earlier stopping distances are achievable while maintaining effective FARs. However, because an earlier stopping distance yields less data for feature extraction, there is a general tradeoff between detection performance and stopping distance.