Dim-Moving Point Target Detection Based on Event-Driven Temporal Profile Evaluation

Small target detection is a crucial research field, where small targets in large field-of-view scenarios approximate point targets. This letter presents an event-driven temporal profile evaluation method for detecting dim moving point targets with low signal-to-noise ratio (SNR). First, the method utilizes high-dimensional statistical distribution distance (HSD) to project pixel-level temporal signal features to a higher dimensional feature, identifying potential target pixels through on/off events triggered by the HSD. Second, the proposed peak-valley feature evaluation index (PVFEI) is designed to detect peak and valley features in the HSD of target-present pixels. It incorporates adaptive threshold segmentation to detect dim-moving point targets effectively. Experimental results demonstrate that the proposed method effectively balances detection accuracy and computational efficiency, providing a promising solution for detecting dim moving point targets.