The tracking-by-detection tracking framework usually consists of two stages: drawing samples around the target object and classifying each sample as either the target object or background. Current popular trackers under this framework typically draw many samples from the raw image and feed them into the deep neural networks, resulting in high computational burden and low tracking speed. In this article, we propose an adversarial feature sampling learning (AFSL) method to address this problem. A convolutional neural network is designed, which takes only one cropped image around the target object as input, and samples are collected from the feature maps with spatial bilinear resampling. To enrich the appearance variations of positive samples in the feature space, which has limited spatial resolution, we fuse the high-level features and low-level features to better describe the target by using a generative adversarial network. Extensive experiments on benchmark data sets demonstrate that the proposed ASFL achieves leading tracking accuracy while significantly accelerating the speed of tracking-by-detection trackers.

中文翻译：

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用于高效视觉跟踪的对抗性特征采样学习


通过检测进行跟踪的跟踪框架通常由两个阶段组成：在目标对象周围绘制样本并将每个样本分类为目标对象或背景。目前流行的跟踪器在该框架下通常会从原始图像中提取许多样本并将其输入深度神经网络，从而导致计算负担较高且跟踪速度较低。在本文中，我们提出了一种对抗性特征采样学习（AFSL）方法来解决这个问题。设计了一种卷积神经网络，仅将目标对象周围的一张裁剪图像作为输入，并通过空间双线性重采样从特征图中收集样本。为了丰富空间分辨率有限的特征空间中正样本的外观变化，我们利用生成对抗网络融合高级特征和低级特征，以更好地描述目标。对基准数据集的大量实验表明，所提出的 ASFL 实现了领先的跟踪精度，同时显着加快了检测跟踪器的跟踪速度。