A Method for Extracting Small Moving Targets Based on Temporal and Spatial Saliency Features

To address the problem of detecting dim small targets against complex moving backgrounds, based on spatiotemporal saliency features, this paper proposes a novel extraction model: the temporal and spatial saliency feature (TSSF) model. In the temporal domain processing stage, a temporal motion saliency operator is constructed by leveraging the differences in temporal motion responses between the target and the background; this operator simultaneously filters for candidate target points while suppressing large-area background clutter. In the spatial domain processing stage, a max-mean-based grayscale saliency operator is constructed to further validate the candidate target points. Building upon target enhancement processing, target trajectories can be effectively extracted through multi-frame association. Quantitative experiments on ten test sequences with diverse background complexities and signal-to-clutter ratios (SCRs) of around 1 or lower show that the TSSF model achieves optimal background suppression factors (BSFs) on seven sequences and the maximum SCR gain (SCRG) across eight sequences, with an average SCRG of roughly 330. For detection performance assessed via ROC curves, the proposed method obtains the highest AUC values on nine test sequences. The consistently superior performance validates the effectiveness and generalizability of the proposed TSSF model for dim small target enhancement, background suppression, and robust target detection.