The methods combining correlation filters (CFs) with the features of convolutional neural network (CNN) are good at object tracking. However, the high-level features of a typical CNN without residual structure suffer from the shortage of fine-grained information, it is easily affected by similar objects or background noise. Meanwhile, CF-based methods usually update filters at every frame even when occlusion occurs, which degrades the capability of discriminating the target from background. A novel scale-adaptive object-tracking method is proposed in this paper. Firstly, the features are extracted from different layers of ResNet to produce response maps, and then, in order to locate the target more accurately, these response maps are fused based on AdaBoost algorithm. Secondly, to prevent the filters from updating when occlusion occurs, an update strategy with occlusion detection is proposed. Finally, a scale filter is used to estimate the target scale. The experimental results demonstrate that the proposed method performs favorably compared with several mainstream methods especially in the case of occlusion and scale change.

中文翻译：

---

具有遮挡检测的尺度自适应目标跟踪算法

结合相关滤波器（CFs）和卷积神经网络（CNN）特征的方法擅长于对象跟踪。但是，没有残差结构的典型CNN的高级功能会遇到缺少细粒度信息的问题，很容易受到相似对象或背景噪声的影响。同时，基于CF的方法通常即使在发生遮挡时也会在每帧更新滤镜，这会降低将目标与背景区分开的能力。提出了一种新的尺度自适应目标跟踪方法。首先，从ResNet的不同层提取特征以生成响应图，然后，为了更精确地定位目标，基于AdaBoost算法对这些响应图进行融合。其次，为防止过滤器在发生阻塞时更新，提出了一种具有遮挡检测的更新策略。最后，比例尺过滤器用于估计目标比例尺。实验结果表明，与几种主流方法相比，该方法具有更好的性能，尤其是在遮挡和尺度变化的情况下。