A compressive imaging model is proposed that multiplexes segments of the field of view (FOV) onto an infrared focal plane array (FPA). Similar to compound imaging, our model is based on combining pixels from a surface comprising of the different parts of the FOV. We formalize this superposition of pixels in a global multiplexing process reducing the number of detectors required for the FPA. We present an analysis of the signal-to-noise ratio for the full rank and compressive collection paradigms for a target detection and tracking scenario. We then apply automated target detection algorithms directly on the measurement sequence for this multiplexing model. We extend the target training and detection processes for the application directly on the encoded measurements. Optimal measurement codes for this application may imply abandoning the ability to reconstruct the actual scene in favor of reconstructing the locations of interesting objects. We present a simulated case study as well as real data results from a visible FOV multiplexing camera.