Infrared Moving Small Target Detection Based on Space–Time Combination in Complex Scenes

Abstract

In the infrared small target images with complex backgrounds, there exist various interferences that share similar characteristics with the target (such as building edges). The accurate detection of small targets is crucial in applications involving infrared search and tracking. However, traditional detection methods based on small target feature detection in a single frame image may result in higher error rates due to insufficient features. Therefore, in this paper, we propose an infrared moving object detection method that integrates spatio-temporal information. To address the limitations of single-frame detection, we introduce a temporal sequence of images to suppress false alarms caused by single-frame detection through analyzing motion features within the sequence. Firstly, based on spatial feature detection, we propose a multi-scale layered contrast feature (MLCF) filtering for preliminary target extraction. Secondly, we utilize the spatio-temporal context (STC) as a feature to track the image sequence point by point, obtaining global motion features. Statistical characteristics are calculated to obtain motion vector data that correspond to abnormal motion, enabling the accurate localization of moving targets. Finally, by combining spatial and temporal features, we determine the precise positions of the targets. The effectiveness of our method is evaluated using a real infrared dataset. Through analysis of the experimental results, our approach demonstrates stronger background suppression capabilities and lower false alarm rates compared to other existing methods. Moreover, our detection rate is similar or even superior to these algorithms, providing further evidence of the efficacy of our algorithm.

Anisotropic Filtering Based on WY Distribution and Multi-scale Energy Concentration Accumulation Method for Dim and Small Target Enhancement

“…Due to the limited utilization information of single-frame processing method, some studies have extended single-frame processing method to the time domain [10][11][12][13][14]. The commonly used research methods in this scenario are mainly time-space combination method [15], that is, the combination of single-frame processing method and multi-frame accumulation method, which mainly includes timespace contrast method, time-space tensor method and other time-space combination methods.…”

Multiscale Feature Extraction U-Net for Infrared Dim- and Small-Target Detection

“…They can thus only be used in specific scenes to suppress the background of a gentle change and cannot solve the problem of complex background [6]. The LCM-based methods take advantage of the difference in gray values between the target and the background to boost the gray values of the target while reducing those of the background, but good detection results can mostly only be obtained when there is high image contrast, so the algorithm's generalization ability is poor, and it cannot be effectively applied to complex backgrounds [7]. Data structure-based methods mainly transform the IDST detection problem into a convex optimization problem with low-rank and sparse matrix recovery.…”

TFCD-Net: Target and False Alarm Collaborative Detection Network for Infrared Imagery

“…Infrared small target detection algorithms can be broadly categorized into two approaches: multi-frame-based and single-frame-based [15]. Multi-frame-based methods detect targets by exploiting the relative motion between targets and background across frames [16][17][18][19][20][21], assuming a relatively static background and necessitating the accumulation of information over multiple frames to pinpoint the target's location. However, there is a pressing need for rapid target detection in practical scenes [22].…”