Multiple target detection in video using quadratic multi-frame correlation filtering

Most integrated target detection and tracking systems employ state-space models to keep track of an explicit number of individual targets. Recently, a non-state-space framework was developed for enhancing target detection in video by applying probabilistic motion models to the soft information in correlation outputs before thresholding. This framework has been referred to as multi-frame correlation filtering (MFCF), and because it avoids the use of state-space models and the formation of explicit tracks, the framework is well-suited for handling scenes with unknown numbers of targets at unknown positions. In this paper, we propose to use quadratic correlation filters (QCFs) in the MFCF framework for robust target detection. We test our detection algorithm on real and synthesized single-target and multi-target video sequences. Simulation results show that MFCF can significantly reduce (to zero in the best case) the false alarm rates of QCFs at detection rates above 95% in the presence of large amounts of uncorrelated noise. We also show that MFCF is more adept at rejecting those false peaks due to uncorrelated noise rather than those due to clutter and compression noise; consequently, we show that filters used in the framework should be made to favor clutter rejection over noise tolerance.