In this paper, we present a vehicular buried threat detection approach developed over the past several years, and its latest implementation and integration in VPEF environment. Buried threats have varying signatures under different operation environment. To reliably detect the true targets and minimizing the number of false alarms, a suite of false alarm mitigators (FAMs) have been developed to process the potential targets identified by the baseline module. A vehicle track can be formed over a number of frames and targets are further analyzed both spatially and temporally. Algorithms have been implemented in C/C++ as GStreamer plugins and are suitable for vehicle mounted, on-the-move realtime exploitation.
We present a system for people counting and re-identification. It can be used by transit and homeland security agencies. Under FTA SBIR program, we have developed a preliminary system for transit passenger counting and re-identification using a laser scanner and video camera. The laser scanner is used to identify the locations of passenger's head and shoulder in an image, a challenging task in crowed environment. It can also estimate the passenger height without prior calibration. Various color models have been applied to form color signatures. Finally, using a statistical fusion and classification scheme, passengers are counted and re-identified.
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