Direct tracking from compressive imagers: A proof of concept

The compressive sensing paradigm holds promise for more cost-effective imaging outside of the visible range, particularly in infrared wavelengths. However, the process of reconstructing compressively sensed images remains computationally expensive. The proof-of-concept tracker described here uses a particle filter with a likelihood update based on a “smashed filter” which estimates correlation directly, avoiding the reconstruction step. This approach leads to increased noise in correlation estimates, but by implementing the track-before-detect concept in the particle filter, tracker convergence may still be achieved with reasonable sensing rates. The tracker has been successfully tested on sequences of moving cars in the PETS2000 dataset.