Transferring Rich Feature Hierarchies for Robust Visual Tracking
Naiyan Wang† Siyi Li† Abhinav Gupta‡ Dit-Yan Yeung† † Hong Kong University of Science and Technology ‡ Carnegie Mellon University
Abstract
Convolutional neural network (CNN) models have demonstrated great success in various computer vision tasks including image classification and object detection. However, some equally important tasks such as visual tracking remain relatively unexplored. We believe that a major hurdle that hinders the application of CNN to visual tracking is the lack of properly labeled training data. While existing applications that liberate the power of CNN often need an enormous amount of training data in the order of millions, visual tracking applications typically have only one labeled example in the first frame of each video. We address this research issue here by pre-training a CNN offline and then transferring the rich feature hierarchies learned to online tracking. The CNN is also fine-tuned during online tracking to adapt to the appearance of the tracked target specified in the first video frame. To fit the characteristics of object tracking, we first pre-train the CNN to recognize what is an object, and then propose to generate a probability map instead of producing a simple class label. Using two challenging open benchmarks for performance evaluation, our proposed tracker has demonstrated
中文速览
卷积神经网络（CNN）在图像分类和目标检测上表现出色，却迟迟未能用于视觉跟踪（visual tracking），根本原因在于跟踪任务只有第一帧的一个标注样本，根本不够训练大规模CNN。为此，作者提出了一种结构化输出深度学习跟踪器（SO-DLT）：先在ImageNet检测数据集上离线预训练CNN，让它学会区分"是不是一个目标"（objectness）的通用特征，输出的不是单一类别标签，而是一张50×50的概率图，逐像素指示目标所在区域；在线跟踪时再用第一帧标注对CNN进行微调，使其专注于当前被跟踪目标，并同时维护两个更新策略不同的CNN以防模型漂移。在两个公开基准上的评测显示，该方法将重叠率曲线下面积（AUC）从0.529提升至0.602，比当时最优跟踪器高出10%以上，证明了通过离线预训练与在线微调相结合的方式，完全可以把CNN的强大表征能力引入训练数据极度匮乏的视觉跟踪任务。
原文 arXiv:1501.04587;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1501.04587v2