TRACE: Lightweight Infrared Small Moving Target Detection under Heavy Noise with Temporal Templates and Neighborhood Evidence

Infrared small moving targets occupy only a few pixels, lack texture and shape information, and are often buried in strong noise and complex backgrounds, making reliable detection highly challenging. Existing multi-frame methods improve detection by exploiting inter-frame motion, and per-pixel temporal methods among them achieve leading results. However, these methods generally process multi-frame features throughout the network, resulting in slow inference and high memory consumption, and they struggle to distinguish targets from noise fluctuations under heavy noise, where the temporal change of a target pixel is often weaker than that of a noise pixel. We observe that a moving target changes neighboring pixels one after another, whereas a noise fluctuation stays on a single pixel, and propose TRACE, a lightweight detection network built on this difference. After standardizing each pixel against its own intensity history in the temporal window, TRACE compresses the multi-frame sequence into two-dimensional features using shared learnable temporal templates in the first layer, accumulating weak target signals while avoiding layer-by-layer multi-frame processing. It then aggregates evidence over a small spatial neighborhood, following the target track and suppressing false alarms caused by noise. Experiments on public datasets show that TRACE outperforms representative multi-frame methods, particularly under heavy noise, and that, compared with the leading per-pixel temporal method, it requires fewer parameters and less memory while running faster. This work offers a new perspective on efficient and robust infrared small moving target detection.