SRRT：探索视觉目标跟踪中的搜索区域调控
SRRT: Exploring Search Region Regulation for Visual Object Tracking
AI总结：
本文提出搜索区域调控跟踪范式SRRT，通过动态估计每帧最优搜索区域并结合锁定状态更新策略，在多个基准上显著提升跟踪性能。
摘要：
主流跟踪器基于上一帧预测或初始边界框生成固定尺寸的矩形区域作为模型输入，即搜索区域。虽然这种方式能获得较好的跟踪效率，但固定尺寸的搜索区域缺乏灵活性，在某些情况下容易失败，例如快速运动和干扰物干扰。由于搜索区域有限，跟踪器容易丢失目标；而搜索区域过大时，又会受到干扰物的影响。受人类跟踪目标模式的启发，我们提出了一种新的跟踪范式，称为搜索区域调控跟踪（SRRT），它在目标被捕获时采用较小的注视范围，并在目标即将丢失时扩大搜索视野。SRRT应用所提出的搜索区域调控器为每一帧动态估计最优搜索区域，从而使跟踪器能够灵活应对目标出现位置的瞬时变化。为了适应在线跟踪过程中目标外观的变化，我们进一步提出了一种锁定状态决定的参考帧更新策略。所提出的SRRT简洁明了，没有多余的复杂设计，却在八个基准上取得了明显的改进，并与其他最先进的跟踪器取得了具有竞争力的结果。在大规模LaSOT基准上，SRRT将SiamRPN++和TransT的AUC绝对增益分别提高了4.6%和3.1%。代码和模型将公开发布。
英文摘要：
The dominant trackers generate a fixed-size rectangular region based on the previous prediction or initial bounding box as the model input, i.e., search region. While this manner obtains promising tracking efficiency, a fixed-size search region lacks flexibility and is likely to fail in some cases, e.g., fast motion and distractor interference. Trackers tend to lose the target object due to the limited search region or experience interference from distractors due to the excessive search region. Drawing inspiration from the pattern humans track an object, we propose a novel tracking paradigm, called Search Region Regulation Tracking (SRRT) that applies a small eyereach when the target is captured and zooms out the search field when the target is about to be lost. SRRT applies a proposed search region regulator to estimate an optimal search region dynamically for each frame, by which the tracker can flexibly respond to transient changes in the location of object occurrences. To adapt the object's appearance variation during online tracking, we further propose a lockingstate determined updating strategy for reference frame updating. The proposed SRRT is concise without bells and whistles, yet achieves evident improvements and competitive results with other state-of-the-art trackers on eight benchmarks. On the large-scale LaSOT benchmark, SRRT improves SiamRPN++ and TransT with absolute gains of 4.6% and 3.1% in terms of AUC. The code and models will be released.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
