Effective detection of small infrared (IR) targets buried in complex backgrounds and heavy noise plays an important role in IR search and track (IRST) systems. In this letter, an IR small target detection method called adaptive scale patch-based contrast measure (ASPCM) is proposed. Compared with existing detection methods based on the human visual system (HVS), our method can estimate the size of the potential target at each location. According to the estimated size, the local contrast between the target and the background is greatly enhanced, and the background clutter can be further suppressed. Experimental results on four real sequences of different complex background demonstrate that the proposed method can detect targets effectively and efficiently, even in the case of complex backgrounds and heavy noises.

Local contrast measure (LCM) proves effective in infrared (IR) small target detection. Existing LCM-based methods focus on mining local features of small targets to improve detection performance. As a result, they struggle to reduce false alarms while maintaining detection rates, especially with high-contrast background interference. To address this issue, this letter proposes global sparsity (GS)-weighted LCM (GSWLCM), which fuses both global and local features of small targets. First, robust LCM (RLCM) is proposed to remove low-contrast backgrounds and extract candidate targets. Then, to suppress high-contrast backgrounds, we customize the random walker (RW) to extract candidate target pixels and construct the global histogram and calculate GS. Finally, GSWLCM fusing global and local features is calculated and the target is detected by adaptive threshold segmentation. Extensive experimental results show that the proposed method is effective in suppressing high-contrast backgrounds and has better detection performance than several state-of-the-art methods.

Thermal infrared (TIR) target tracking is an important topic in the computer vision area. The TIR images are not affected by ambient light and have strong environmental adaptability, making them widely used in battlefield perception, video surveillance, assisted driving, etc. However, TIR target tracking faces problems such as relatively insufficient information and lack of target texture information, which significantly affects the tracking accuracy of the TIR tracking methods. To solve the above problems, we propose a TIR target tracking method based on a Siamese network with a hierarchical attention mechanism (called: SiamHAN). Specifically, the CIoU Loss is introduced to make full use of the regression box information to calculate the loss function more accurately. The GCNet attention mechanism is introduced to reconstruct the feature extraction structure of fine-grained information for the fine-grained information of thermal infrared images. Meanwhile, for the feature information of the hierarchical backbone network of the Siamese network, the ECANet attention mechanism is used for hierarchical feature fusion, so that it can fully utilize the feature information of the multi-layer backbone network to represent the target. On the LSOTB-TIR, the hierarchical attention Siamese network achieved a 2.9% increase in success rate and a 4.3% increase in precision relative to the baseline tracker. Experiments show that the proposed SiamHAN method has achieved competitive tracking results on the thermal infrared testing datasets.

Infrared small target detection is critical to infrared search and tracking systems. However, accurate and robust detection remains challenging due to the scarcity of target information and the complexity of clutter interference. Existing methods have some limitations in feature representation, leading to poor detection performance in complex scenes. Especially when there are sharp edges near the target or in cluster multitarget detection, the “target suppression” phenomenon tends to occur. To address this issue, we propose a robust unsupervised multifeature representation (RUMFR) method for infrared small target detection. On the one hand, robust unsupervised spatial clustering (RUSC) is designed to improve the accuracy of feature extraction; on the other hand, pixel-level multiple feature representation is proposed to fully utilize the target detail information. Specifically, we first propose the center-weighted interclass difference measure (CWIDM) with a trilayer design for fast candidate target extraction. Note that CWIDM also guides the parameter settings of RUSC. Then, the RUSC-based model is constructed to accurately extract target features in complex scenes. By designing the parameter adaptive strategy and iterative clustering strategy, RUSC can robustly segment cluster multitargets from complex backgrounds. Finally, RUMFR that fuses pixel-level contrast, distribution, and directional gradient features is proposed for better target representation and clutter suppression. Extensive experimental results show that our method has stronger feature representation capability and achieves better detection performance than several state-of-the-art methods.