The features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model’s high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to obtain the appropriate estimation. How to strike a compromise between high robustness and estimation accuracy while designing filters has always been challenging. The H-infinity filter is a widely used robust algorithm. Based on the H-infinity cubature Kalman filter (HCKF), a novel adaptive robust cubature Kalman filter (ARCKF) is suggested in this paper. There are two adaptable components in the algorithm. First, an adaptive fading factor addresses the model uncertainty issue brought on by the target’s maneuvering turn. Second, an improved Sage–Husa estimation based on the Mahalanobis distance (MD) is suggested to estimate the measurement noise covariance matrix adaptively. The new approach significantly increases the robustness and estimation precision of the HCKF. According to the simulation results, the suggested algorithm is more effective than the conventional HCKF at handling system model errors and abnormal observations.

The maximum correntropy criterion (MCC), as an effective method for dealing with anomalous measurement noise, is widely applied in the design of filters. However, its performance largely depends on the proper setting of the kernel bandwidth, and currently, there is no efficient adaptive kernel adjustment mechanism. To deal with this issue, a new adaptive Cauchy-kernel maximum correntropy cubature Kalman filter (ACKMC-CKF) is proposed. This algorithm constructs adaptive factors for each dimension of the measurement system and establishes an entropy matrix with adaptive kernel sizes, enabling targeted handling of specific anomalies. Through simulation experiments in target tracking, the performance of the proposed algorithm was comprehensively validated. The results show that the ACKMC-CKF, through its flexible kernel adaptive mechanism, can effectively handle various types of anomalies. Not only does the algorithm demonstrate excellent reliability, but it also has low sensitivity to parameter settings, making it more broadly applicable in a variety of practical application scenarios.

The task of 3D reconstruction of urban targets holds pivotal importance for various applications, including autonomous driving, digital twin technology, and urban planning and development. The intricate nature of urban landscapes presents substantial challenges in attaining 3D reconstructions with high precision. In this paper, we propose a semantically aware multi-view 3D reconstruction method for urban applications which incorporates semantic information into the technical 3D reconstruction. Our research primarily focuses on two major components: sparse reconstruction and dense reconstruction. For the sparse reconstruction process, we present a semantic consistency-based error filtering approach for feature matching. To address the challenge of errors introduced by the presence of numerous dynamic objects in an urban scene, which affects the Structure-from-Motion (SfM) process, we propose a computation strategy based on dynamic–static separation to effectively eliminate mismatches. For the dense reconstruction process, we present a semantic-based Semi-Global Matching (sSGM) method. This method leverages semantic consistency to assess depth continuity, thereby enhancing the cost function during depth estimation. The improved sSGM method not only significantly enhances the accuracy of reconstructing the edges of the targets but also yields a dense point cloud containing semantic information. Through validation using architectural datasets, the proposed method was found to increase the reconstruction accuracy by 32.79% compared to the original SGM, and by 63.06% compared to the PatchMatch method. Therefore, the proposed reconstruction method holds significant potential in urban applications.

Multi-Object Tracking (MOT) remains a fundamental challenge in computer vision, with significant applications in autonomous systems, surveillance, and intelligent transportation. The Tracking-By-Detection (TBD) paradigm has achieved notable tracking accuracy; however, its performance is highly sensitive to low-confidence detections (e.g., partial occlusions or blurred objects). Existing methods, such as ByteTrack, utilize a dual-threshold strategy for the filtering of detections, but determining the optimal high-confidence threshold is computationally expensive and lacks robustness across different scenarios. In this study, we analyze the sensitivity of state-of-the-art (SOTA) TBD-based trackers to variations in the high-confidence threshold and introduce SolidTrack, an enhanced tracking algorithm integrating SOLIDER-ReID, a transformer-based re-identification model. Our approach mitigates the impact of threshold variations by improving feature extraction, particularly under challenging conditions such as low-confidence detections and occlusions. Experimental evaluations demonstrate that SolidTrack exhibits superior robustness across varying threshold settings. Additionally, SolidTrack achieves SOTA performance on the test sets of the MOT17 and MOT20 benchmarks, achieving an MOTA of 80.6, IDF1 of 80.0, and HOTA of 65.0 on MOT17 and an MOTA of 77.9, IDF1 of 77.5, and HOTA of 63.4 on MOT20.

Siamese network object tracking is a widely employed tracking method due to its simplicity and effectiveness. It first employs a two-stream network to independently extract template and search region features. Subsequently, these features are then combined through feature association to yield object information within the visual scene. However, the conventional approach faces limitations when it leverages the template features as a convolution kernel to convolve the search image features, which restricts the ability to capture complex and nonlinear feature transformations of objects, thereby restricting its discriminative capabilities. To overcome this challenge, we propose replacing traditional convolutional correlation with Slot Attention for feature association. This novel approach enables the effective extraction of nonlinear features within the scene, while augmenting the discriminative capacity. Furthermore, to increase the inference efficiency and reduce the parameter occupation, we suggest deploying a single Slot Attention module for multiple associations. Our tracking algorithm, SiamSlot, was evaluated on diverse benchmarks, including VOT2019, GOT-10k, UAV123, and Nfs. The experiments show a remarkable improvement in performance relative to previous methods under the same network size.

Infrared imaging plays a vital role in critical surveillance, military reconnaissance, and industrial inspection applications due to its advantages such as strong concealment and the ability to operate around the clock. However, the combination of low infrared image resolution and complex background scenarios poses significant challenges for traditional deep learning models in accurately extracting the most discriminative features for classification. These models are often disrupted by irrelevant features, especially when data for new classes is scarce. Current few-shot learning approaches heavily rely on comparing image patches, but the scarcity of data can significantly degrade the performance of recognition algorithms. To address these challenges, we propose the Class Patch Similarity Weighted Embedding (CPSWE) framework for few-shot infrared target classification. The CPSWE framework employs a ViT architecture for feature extraction. By introducing class embeddings and calculating similarity-based weights for each patch, CPSWE reweights the patch features to enhance their relevance to the target class. This approach improves the discriminability of class-related features, leading to better generalization in few-shot settings. Furthermore, we introduce an infrared dataset specifically designed for few-shot learning, combining multiple open-source datasets to support research in this area. Extensive experiments on the few-shot learning benchmark dataset miniImageNet and the infrared dataset miniIRNet show that CPSWE outperforms existing few-shot learning methods, achieving significant improvements in classification accuracy on infrared image datasets with limited labeled samples.