TL;DR
The proposed BFA-HARF Tracker effectively alleviates bounding box ambiguity and tracking drift, achieving performance comparable to state-of-the-art methods.
This study proposes a new RGB-T target tracking algorithm, CLRFI, which utilizes the Vision Transformer (ViT) architecture for RGB-T tracking scenarios and implements a comparative learning strategy focused on the target region, thereby optimizing the target’s representation.
Robust and reliable object detection under adverse conditions remains a critical challenge for automated driving systems (ADS). The performance of RGB-based (visible-spectrum) cameras degrades in poor lighting conditions, whereas the performance of RGB-Long-Wave Infrared (LWIR) fusion architectures remains limited in a...
Object detection using visible–infrared images has become increasingly important for all-day detection scenarios. However, due to significant imaging discrepancies between the visible and infrared modalities, achieving accurate modal alignment and effective feature fusion remains a major challenge. Existing methods oft...
RGB-T tracking is a challenging task due to the various obstacles, including occlusion, illumination, thermal crossover, and so on. However, existing research focuses purely on multimodal interaction or fusion design, while overlooking the effective handling of robustness in the face of challenges. Additionally, these...
Multimodal perception integrating light detection and ranging (LiDAR) and cameras has become a key paradigm for 3-D object detection, as it leverages both geometric structure and semantic information. However, in real-world autonomous driving scenarios, calibration errors, adverse weather, and sensor degradation can in...
In recent years, prompt learning-based RGB-T tracking methods have attracted growing attention due to their ability to transfer knowledge from pre-trained vision models to tracking tasks, thereby significantly improving tracking accuracy. However, most of these methods predominantly focus on spatial-domain feature extr...
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