Low-Altitude Infrared Slow-Moving Small Target Detection via Spatial-Temporal Features Measure

Abstract

Robust detection of infrared slow-moving small targets is crucial in infrared search and tracking (IRST) applications such as infrared guidance and low-altitude security; however, existing methods easily cause missed detection and false alarms when detecting infrared small targets in complex low-altitude scenes. In this article, a new low-altitude slow-moving small target detection algorithm based on spatial-temporal features measure (STFM) is proposed. First, we construct a circular kernel to calculate the local grayscale difference (LGD) in a single image, which is essential to suppress low-frequency background and irregular edges in the spatial domain. Then, a short-term energy aggregation (SEA) mechanism with the accumulation of the moving target energy in multiple successive frames is proposed to enhance the dim target. Next, the spatial-temporal saliency map (STSM) is obtained by integrating the two above operations, and the candidate targets are segmented using an adaptive threshold mechanism from STSM. Finally, a long-term trajectory continuity (LTC) measurement is designed to confirm the real target and further eliminate false alarms. The SEA and LTC modules exploit the local inconsistency and the trajectory continuity of the moving small target in the temporal domain, respectively. Experimental results on six infrared image sequences containing different low-altitude scenes demonstrate the effectiveness of the proposed method, which performs better than the existing state-of-the-art methods.

Robust Low-Sidelobe Transmit Beamforming under Peak-to-Average-Power Ratio Constraint

“…Generally, "LSS target" is a collective term for a variety of small craft and air drifters that fly at low or ultra-low altitudes and have slow speeds and other characteristics. Radar detection is now quite a popular means of LSS target detection compared with acoustic detection [37] and photoelectric detection [38] because of its advantages of all-time, all-weather operation and fast response time. However, it faces difficulties in the complex urban background where the target is located, such as fast-moving interference, strong ground clutter and noise, and a high-complexity environment, resulting in the low SNR of the radar echo signal and affecting the detection performance of the radar.…”

A Software-Defined Radar for Low-Altitude Slow-Moving Small Targets Detection Using Transmit Beam Control

“…Numerous studies have been carried out to improve the detection of LSS targets utilizing ground-based radar in complex environments. In summary, the LSS target detection strategies can be categorized into three types: methods based on sophisticated signal pro-cessing algorithms, methods based on transmit/receive beam optimization, and methods based on the optimization or innovation of radar systems [10].…”

Infrared Target Detection Based on Joint Spatio-Temporal Filtering and L1 Norm Regularization

“…However, the calculation of total variation will make the restored image excessive reduction and the loss of detailed information of the image, that is not conducive to target detection in complex scenes. In the spatial-temporal features measure model (STFM) proposed by Mu [15], firstly, local grayscale difference model is proposed based on the local information of target imaging to analyze the local information, and then short-term energy aggregation model is proposed based on the local information to enhance the energy of dim and small targets in the local region of interest. Finally, combined with energy enhancement and local information analysis, the ong-term trajectory continuity detection model is proposed to obtain the difference image, and perfect results are achieved.…”