Despite many years of research, the accuracy of small target detection is still restricted by the complex background. In addition, the contradiction between the computation complexity reduction and detection performance improvement makes it hard to apply some high-performance methods towards practical application. In this letter, a novel group image-patch tensor model (GIPT) is proposed to solve the above problems. Firstly, based on the structure tensor theory, a newly designed measurement criterion is employed to divide the image pixels into three types: point area, flat area, and line area. Second, image pixels with line priors and point priors are assigned to different weights to reduce the effects of strong background edges. Third, a group image-patch tensor model is proposed to better explore the low-rank of the background, in which patches with the same feature type are rearranged into the same group for tensor decomposition. Moreover, the proposed GIPT model can make the optimization process run in parallel, which can greatly reduce the complexity of the algorithms. The experiment results show the superiority of the proposed method compared with other state-of-the-art algorithms.

Small target detection against strong vignetting background poses two major challenges: the submergence of the target in high brightness background and the difficult separability between dense noise and weak target. These challenges have not been adequately addressed by existing methods, which heavily rely on the low-rank prior of background. In this paper, our focus shifts to the smoothness of background and propose a dynamic weight guided smooth-sparse decomposition model (DW-SSD). First, we adopt a surface-fitting perspective to reconstruct the smooth vignetting, where B-spline is introduced as the basis, and total variation (TV) is utilized to regularize the coefficients of the B-spline basis. This term provides a flexible description for different background local regions with low computation cost, effectively suppressing the high brightness vignetting and facilitating the emergence of target. Second, a dynamic weight based on spatial-temporal prior characteristic is added to the target component, where the differences in spatial contrast and temporal profile are jointly considered to guide a precise separation of the target from dense noise. Finally, a customized solution is designed based on the alternating direction multiplier method (ADMM) to separate small targets from strong vignetting rapidly and precisely. Comprehensive experiments on five real sequences demonstrate that DW-SSD method effectively overcomes the challenges posed by strong vignetting and performs well in real-time small target detection. The code is publicly available at https://github.com/D-IceIce/DW-SSD.

The detection of an infrared small target faces the problems of background interference and non-obvious target features, which have yet to be efficiently solved. By employing the non-local self-correlation characteristic of the infrared images, the principle component pursuit (PCP)-based methods are demonstrated to be applicable to infrared small target detection in a complex scene. However, existing PCP-based methods heavily depend on the uniform distribution of the background pixels and are prone to generating a high number of false alarms under strong clutter situations. In this paper, we propose a group low-rank regularized principle component pursuit model (GPCP) to solve this problem. First, the local image patches are clustered into several groups that correspond to different grayscale distributions. These patch groups are regularized with a group low-rank constraint, enabling an independent recovery of different background regions. Then, GPCP model integrates the group low-rank components with a global sparse component to extract small targets from the background. Different singular value thresholds can be exploited for image groups corresponding to different brightness and grayscale variance, boosting the recovery of background clutters and also enhancing the detection of small targets. Finally, a customized optimization approach based on alternating direction method of multipliers is proposed to solve this model. We set three representative detection scenes, including the ground background, sea background and sky background for experiment analysis and model comparison. The evaluation results show the proposed model has superiority in background suppression and achieves better adaptability for different scenes compared with various state-of-the-art methods.

The detection of infrared dim and small targets in complex backgrounds is very challenging because of the low signal-to-noise ratio of targets and the drastic change in background. Low-rank sparse decomposition based on the structural characteristics of infrared images has attracted the attention of many scholars because of its good interpretability. In order to improve the sensitivity of sliding window size, insufficient utilization of time series information, and inaccurate tensor rank in existing methods, a four-dimensional tensor model based on superpixel segmentation and statistical clustering is proposed for infrared dim and small target detection (ISTD). First, the idea of superpixel segmentation is proposed to eliminate the dependence of the algorithm on selection of the sliding window size. Second, based on the improved structure tensor theory, the image pixels are statistically clustered into three types: corner region, flat region, and edge region, and are assigned different weights to reduce the influence of background edges. Next, in order to better use spatiotemporal correlation, a Four-Dimensional Fully-Connected Tensor Network (4D-FCTN) model is proposed in which 3D patches with the same feature types are rearranged into the same group to form a four-dimensional tensor. Finally, the FCTN decomposition method is used to decompose the clustered tensor into low-dimensional tensors, with the alternating direction multiplier method (ADMM) used to decompose the low-rank background part and sparse target part. We validated our model across various datasets, employing cutting-edge methodologies to assess its effectiveness in terms of detection precision and reduction of background interference. A comparative analysis corroborated the superiority of our proposed approach over prevailing techniques.

The one-shot multiple object tracking framework simultaneously generates detected targets and re-identification (ReID) embeddings, and employs them to associate previous tracks. However, the continuity between the learning and extraction within the ReID task is unconsciously neglected because of treating them as two isolated stages. This leads to unreliable ReID embeddings and poses a challenge to target matching especially in complex and crowded scenarios. To this end, we propose a guided embedding enhancement multiple object tracker, named G2EMOT. It consists of three innovative designs aiming at improving the embedding representation capability for individual targets. First, based on the original feature map, a global instance-specific context decoupling (GICD) module is devised to facilitate the respective feature learning for detection and ReID branches. Then, a heatmap guided embedding enhancement (HGEE) module is introduced to connect the processes of embedding learning and extraction, ensuring that the detected target coordinates are accurately aligned with the discriminative ReID embeddings. Finally, to associate and stabilize matched targets, we present a novel two-gate guided embedding update (TG-GEU) strategy that dynamically updates ReID embeddings. With the proposed three components, G2EMOT achieves outstanding performance on popular MOT benchmarks, while outperforming the existing one-shot tracking methods by a large margin. In particular, it realizes IDF1 of 76.1% on MOT17 and IDF1 of 74.2% on MOT20. The source codes are released at https://github.com/ydhcg-BoBo/G2EMOT.

Due to their devastating ability to extract features, convolutional neural network (CNN)-based approaches have achieved tremendous success in hyperspectral image (HSI) classification. However, previous works have been dedicated to constructing deeper or wider deep learning networks to obtain exceptional classification performance, but as the layers get deeper, the gradient disappearance problem impedes the convergence stability of network models. Additionally, previous works usually focused on utilizing fixed-scale convolutional kernels or multiple available, receptive fields with varying scales to capture features, which leads to the underutilization of information and is vulnerable to feature learning. To remedy the above issues, we propose an innovative hybrid-scale feature enhancement network (HFENet) for HSI classification. Specifically, HFENet contains two key modules: a hybrid-scale feature extraction block (HFEB) and a shuffle attention enhancement block (SAEB). HFEB is designed to excavate spectral–spatial structure information of distinct scales, types, and branches, which can augment the multiplicity of spectral–spatial features while modeling the global long-range dependencies of spectral–spatial informative features. SAEB is devised to adaptively recalibrate spectral-wise and spatial-wise feature responses to generate the purified spectral–spatial information, which effectively filters redundant information and noisy pixels and is conducive to enhancing classification performance. Compared with several sophisticated baselines, a series of experiments conducted on three public hyperspectral datasets showed that the accuracies of OA, AA, and Kappa all exceed 99%, demonstrating that the presented HFENet achieves state-of-the-art performance.