Similar object interference (SOI) problem challenges the single object tracking (SOT) task, leading to the failure of feature-based trackers and subsequent performance degradation. Unfortunately, current generic SOT benchmarks do not effectively tackle this critical challenge, while popular SOT algorithms consistently underestimate the influence they have on tracking performance. To bridge this gap and further enhance the investigation of similar object interference in SOT, we adopt the following viewpoints: (1) By examining the operational principles of mainstream trackers and their performance on representative SOT datasets, we redefine similar objects, taking into account the cognitive bias that exists between trackers and humans when dealing with this challenge. (2) Subsequently, we develop a mining methodology that enables the extraction of the SOI sub-dataset from SOT datasets without relying on human intervention. This methodology comprises two main components: determining the SOI challenge and screening the SOI sequences. The SOI dataset is acquired from representative SOT dataset using our proposed approach, known as SOI2023. This dataset serves as an ideal environment to facilitate the investigation of challenges related to similar object interference. (3) Additionally, we conduct extensive tracking experiments with 20 typical trackers and their variants on SOI2023 and analyze their performance for similar object interference scenes in several dimensions. The experimental results demonstrate the effectiveness of our proposed mining method, while revealing the strengths and weaknesses of current trackers when faced with the challenge of similar object interference. We hope this work can provide inspiration to the tracking community and also provide support and insights for robust tracking under the SOI challenge.

In recent years, the development of deep learning algorithms has significantly advanced the application of synthetic aperture radar (SAR) aircraft detection in remote sensing and military fields. However, existing methods face a dual dilemma: CNN‐based models suffer from insufficient detection accuracy due to limitations in local receptive fields, whereas Transformer‐based models improve accuracy by leveraging attention mechanisms but incur significant computational overhead due to their quadratic complexity. This imbalance between accuracy and efficiency severely limits the development of SAR aircraft detection. To address this problem, this paper propose a novel neural network based on state space models (SSM), termed the Mamba SAR detection network (MSAD). Specifically, we design a feature encoding module, MEBlock, that integrates CNN with SSM to enhance global feature modelling capabilities. Meanwhile, the linear computational complexity brought by SSM is superior to that of Transformer architectures, achieving a reduction in computational overhead. Additionally, we propose a context‐aware feature fusion module (CAFF) that combines attention mechanisms to achieve adaptive fusion of multi‐scale features. Lastly, a lightweight parameter‐shared detection head (PSHead) is utilised to effectively reduce redundant parameters through implicit feature interaction. Experiments on the SAR‐AirCraft‐v1.0 and SADD datasets show that MSAD achieves higher accuracy than existing algorithms, whereas its GFLOPs are 2.7 times smaller than those of the Transformer architecture RT‐DETR. These results validate the core role of SSM as an accuracy‐efficiency balancer, reflecting MSAD's perceptual capability and performance in SAR aircraft detection in complex environments.

Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast motion. These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera. To evaluate the robustness of SOT methods, we propose BioDrone -- the first bionic drone-based visual benchmark for SOT. Unlike existing UAV datasets, BioDrone features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. BioDrone hence highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. To date, BioDrone offers the largest UAV-based SOT benchmark with high-quality fine-grained manual annotations and automatically generates frame-level labels, designed for robust vision analyses. Leveraging our proposed BioDrone, we conduct a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method (KeepTrack KeepTrack) for robust SOT. Our evaluation leads to new baselines and insights for robust SOT. Moving forward, we hope that BioDrone will not only serve as a high-quality benchmark for robust SOT, but also invite future research into robust computer vision. The database, toolkits, evaluation server, and baseline results are available at http://biodrone.aitestunion.com.

Multimodal object detection with RGB and thermal infrared imagery is essential for reliable perception in complex environments, but existing methods often remain limited by insufficient cross‐modal feature fusion. To address this issue, we propose PFDet, a progressive cross‐domain feature fusion framework for robust multimodal detection. PFDet progressively aligns semantic cues, interacts cross‐modal features, and refines multi‐scale representations. Specifically, a Semantic Alignment Guidance (SAG) module establishes a unified semantic reference at the input level to guide subsequent fusion. Unified Cross‐Modal Fusion Modules (UCMF) are then deployed across the backbone to enable fine‐grained bidirectional feature interaction, while a Context Guide Fusion Module (CGFM) performs context‐aware multi‐scale refinement in the neck network. Experimental results on the M3FD and FLIR‐Aligned datasets show that PFDet achieves state‐of‐the‐art performance, reaching 86.7% mAP@0.5 on M3FD and 86.9% mAP@0.5 on FLIR‐Aligned, while maintaining real‐time inference speed. Ablation studies further validate the effectiveness of the proposed progressive fusion strategy. This work provides a robust and scalable paradigm for multimodal object detection, with the potential to be extended to other heterogeneous sensing modalities, contributing to reliable perception in complex real‐world environments.