Tracking with a small single Convolutional Neural Network

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Robust visual tracking is a significant but challenging task in computer vision. Deep convolutional neural networks have been proverbially applied to visual tracking in recent years by learning a genetic representation from numerous training images. However, the deep networks training is time-consuming. In this work, an efficient and robust tracking algorithm using a small single Convolutional Neural Network (CNN) is proposed. Different from the existing CNN models, a novel loss function to process image batches in a single branch CNN is introduced. In addition, appropriate model update in a “when required” style is used when tracking to achieve performance boost. Quantitative experimental results on various video sequences demonstrate the superior performance of the proposed method in comparison with other state-of-the-art trackers.