Visual object tracking based on residual network and cascaded correlation filters
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ORIGINAL RESEARCH
Visual object tracking based on residual network and cascaded correlation filters Jianming Zhang1 · Juan Sun1 · Jin Wang1 · Xiao‑Guang Yue2 Received: 4 April 2020 / Accepted: 22 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Significant progress is made in the field of object tracking recently. Especially, trackers based on deep learning and correlation filters both have achieved excellent performance. However, object tracking still faces some challenging problems such as deformation and illumination. In such kinds of situations, the accuracy and precision of tracking algorithms plunge as a result. It is imminent to find a solution to this situation. In this paper, we propose a tracking algorithm based on features extracted by residual network called Resnet features and cascaded correlation filters to improve precision and accuracy. Firstly, features extracted by a deep residual network trained on other image processing datasets, are robust enough and retain higher resolution, therefore, we exploit Resnet-101 pretrained offline to obtain features extracted by middle and high layers for target appearance model representation. Resnet-101 is deeper compared with other deep neural networks which means it contains more semantic information. Then, the method we propose to combine our correlation filters is superior. We propose cascaded correlation filters generated by handcraft, middle-level and high-level features from residual network to gain better competence. Handcraft features localize target precisely because they contain more spatial details while Resnet features are robust to the target appearance change because they retain more semantic information. Finally, we conduct extensive experiments on OTB2013 and OTB2015 benchmark. The experimental results show that our tracker achieves high performance under all kinds of challenges and performs favorably against other state-of-the-art trackers. Keywords Object tracking · Deep learning · Residual network · Resnet features · Cascaded correlation filters
1 Introduction Given the status (specified by a bounding box with coordinate, width and height) of an unknown target in the first frame, object tracking is a task of predicting the status of the target in the following frames. Object tracking is a fundamental but significant problem in the field of computer (Yilmaz et al. 2006; Chen et al. 2019a; Liu et al. 2018, 2019). And it has a wide range of practical applications such as video surveillance system, video-based human–computer * Jianming Zhang [email protected] 1
Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
Rattanakosin International College of Creative Entrepreneurship, Rajamangala University of Technology Rattanakosin, Nakhon Pathom 73170, Thailand
2
interaction, flight control system in modern militarization and aerospace industry.
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