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Convolutional neural network (CNN) models have demonstrated great success invarious computer vision tasks including image classification and objectdetection. However, some equally important tasks such as visual tracking remainrelatively unexplored. We believe that a major hurdle that hinders theapplication of CNN to visual tracking is the lack of properly labeled trainingdata. While existing applications that liberate the power of CNN often need anenormous amount of training data in the order of millions, visual trackingapplications typically have only one labeled example in the first frame of eachvideo. We address this research issue here by pre-training a CNN offline andthen transferring the rich feature hierarchies learned to online tracking. TheCNN is also fine-tuned during online tracking to adapt to the appearance of thetracked target specified in the first video frame. To fit the characteristicsof 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 simpleclass label. Using two challenging open benchmarks for performance evaluation,our proposed tracker has demonstrated substantial improvement over otherstate-of-the-art trackers.

Transferring Rich Feature Hierarchies for Robust Visual Tracking