Conventional discriminative-correlation-filter-based (DCF-based) visual tracking methods always update model at a fixed frequency and learning rate. Without evaluating the tracking confidence scores, the response map generated by filter is the only evidence for locating. Thus, most of the existing DCF-based methods suffer from the model contamination caused by drastic appearance variations, which leads to tracking drift even failure. And excessively frequent update will increase the computational redundancy and risk of over-fitting. In addition, these methods cannot recover target from heavy occlusion neither. Based on the observation that the shape of response maps reflects the matching degree between filter and target, we design and train a small-scale binary network named as response map analysis network (RAN) to evaluate the confidence scores of filters. Further, we propose to learn multiple filters to exploit different kinds of features, and adaptively adjust the update parameters according to the corresponding confidence scores. Moreover, we build a simple occlusion event model to detect heavy occlusion and recover target. Extensive experimental results validate the effectiveness of RAN and demonstrate that the proposed tracker performs favorably against other state-of-the-art (SOTA) DCF-based trackers in terms of precision, overlap rate and efficiency.

传统的基于判别相关滤波器（基于 DCF）的视觉跟踪方法总是以固定的频率和学习率更新模型。在不评估跟踪置信度分数的情况下，过滤器生成的响应图是定位的唯一证据。因此，大多数现有的基于 DCF 的方法都存在由剧烈的外观变化引起的模型污染，导致跟踪漂移甚至失败。而过于频繁的更新会增加计算冗余和过拟合的风险。此外，这些方法也不能从重度遮挡中恢复目标。观察到响应图的形状反映了过滤器和目标之间的匹配程度，我们设计并训练了一个名为响应图分析网络（RAN）的小规模二进制网络来评估过滤器的置信度分数。此外，我们建议学习多个过滤器以利用不同类型的特征，并根据相应的置信度分数自适应地调整更新参数。此外，我们建立了一个简单的遮挡事件模型来检测严重遮挡并恢复目标。大量的实验结果验证了 RAN 的有效性，并证明所提出的跟踪器在精度、重叠率和效率方面优于其他基于最新技术 (SOTA) DCF 的跟踪器。我们构建了一个简单的遮挡事件模型来检测严重遮挡并恢复目标。大量的实验结果验证了 RAN 的有效性，并证明所提出的跟踪器在精度、重叠率和效率方面优于其他基于最新技术 (SOTA) DCF 的跟踪器。我们构建了一个简单的遮挡事件模型来检测严重遮挡并恢复目标。大量的实验结果验证了 RAN 的有效性，并证明所提出的跟踪器在精度、重叠率和效率方面优于其他基于最新技术 (SOTA) DCF 的跟踪器。