A High Framerate Imaging-Based Framework for Moving Point Target Detection in Very Low SNR

This paper proposes a framework for the low-SNR moving point target detection based on temporal profile analysis of high framerate image sequences (HFIF). The main idea is that a weak transient disturbance will appear in the time series of target-present pixels, and change the statistical characteristics of the temporal profile in the very short high-framerate sampling time. In the framework, the target detection workflow, temporal mathematical model of the moving point target, pixel modeling process, key impact factors of detection ability are presented. Firstly, the temporal mathematical model of pixels affected by a moving point target in high framerate image sequences is presented, which gives guidance for designing target detectors in different situations. Then, a pixel modeling approach is proposed to characterize the temporal behavior of pixels for distinguishing between background and target-present pixels. Meanwhile, the key impact factors, such as the framerate, target velocity, spatial resolution, signal to noise ratio(SNR) and target size, on the detection ability are studied. Lastly, a kernel method-based target detector is presented to demonstrate the effectiveness of the framework. The experimental results with both simulated and real-world data demonstrate that the approach can efficiently detect extremely dim targets based on high framerate imaging.