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.

Hyperspectral Imagery (HSI) plays a crucial role in military and civilian target detection. However, HSI target detection remains highly challenging due to the interference caused by complex and diverse real-world scenarios. This paper proposes a Spectral Information Divergence-driven Diffusion Network model (SID-DN) for hyperspectral target detection, which significantly enhances detection robustness in complex scenes by decoupling background distribution modeling from target detection. The proposed method focuses on learning the background distribution in hyperspectral data and achieves target detection by accurately reconstructing background samples to identify differences between background and target samples. This method introduces an adaptive coarse detection module, which optimizes the coarse detection process in generative hyperspectral target detection, effectively reducing the background-target misclassification. Additionally, a SID-based Diffusion model is designed to optimize the loss of Diffusion, effectively reducing the interference of suspected target samples during the background learning process. Experiments on three real-world datasets demonstrate that the method is highly competitive, with detection results significantly outperforming current state-of-the-art methods.

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.