Online Model Adaptation for UAV Tracking with Convolutional Neural Network

Abstract

Unmanned aerial vehicle (UAV) tracking is a challenging problem and a core component of UAV applications. CNNs have shown impressive performance in computer vision applications, such as object detection, image classification and so on. In this work, a locally connected layer is employed in a CNN architecture to extract robust features. We also utilize focal loss function to focus training on hard examples. Our CNN is first pre-trained offline to learn robust features. The training data is classified according to the texture, color, size of the target and the background information properties. In a subsequent online tracking phase, this CNN is fine-tuned to adapt to the appearance changes of the tracked target. We applied this approach to the problem of UAV tracking and performed extensive experimental results on large scale benchmark datasets. Results obtained show that the proposed method performs favorably against the state-of-the-art trackers in terms of accuracy, robustness and efficiency.