LLOT

In recent years, the field of visual tracking has made significant progress with the application of large-scale training datasets. These datasets have supported the development of sophisticated algorithms, enhancing the accuracy and stability of visual object tracking. However, most research has primarily focused on favorable illumination circumstances, neglecting the challenges of tracking in low-ligh environments. In low-light scenes, lighting may change dramatically, targets may lack distinct texture features, and in some scenarios, targets may not be directly observable. These factors can lead to a severe decline in tracking performance. To address this issue, we introduce LLOT, a benchmark specifically designed for Low-Light Object Tracking. LLOT comprises 269 challenging sequences with a total of over 132K frames, each carefully annotated with bounding boxes. This specially designed dataset aims to promote innovation and advancement in object tracking techniques for low-light conditions, addressing challenges not adequately covered by existing benchmarks. To assess the performance of existing methods on LLOT, we conducted extensive tests on 39 state-of-the-art tracking algorithms. The results highlight a considerable gap in low-light tracking performance. In response, we propose H-DCPT, a novel tracker that incorporates historical and darkness clue prompts to set a stronger baseline. H-DCPT outperformed all 39 evaluated methods in our experiments, demonstrating significant improvements.We hope that our benchmark and H-DCPT will stimulate the development of novel and accurate methods for tracking objects in low-light conditions.

近年来，随着大规模训练数据集的应用，视觉跟踪领域取得了显著的进展。这些数据集支持了复杂算法的发展，提高了视觉目标跟踪的准确性和稳定性。然而，大多数研究主要集中于有利的光照条件下，忽视了低光照环境中的跟踪挑战。在低光照场景中，光线可能发生剧烈变化，目标可能缺乏明显的纹理特征，在某些情况下，目标可能无法直接观察。这些因素可能导致跟踪性能的严重下降。为解决这一问题，我们提出了LLOT（低光照目标跟踪基准），该基准特别针对低光照条件下的目标跟踪。LLOT包含269个具有挑战性的序列，总共有超过132K帧，每一帧都经过精心标注的边界框。这个特别设计的数据库旨在推动低光照条件下目标跟踪技术的创新与进步，解决现有基准未能充分覆盖的挑战。为了评估现有方法在LLOT上的性能，我们对39个最先进的跟踪算法进行了广泛的测试。结果突显了低光照跟踪性能存在较大差距。为此，我们提出了H-DCPT（历史与黑暗线索提示跟踪器），这是一种新颖的跟踪器，它结合了历史和黑暗线索提示来设定更强的基线。在实验中，H-DCPT优于所有39个评估的方法，展现了显著的改进。我们希望我们的基准和H-DCPT能够激发出针对低光照条件下跟踪对象的创新且准确的方法。