Abstract
<h2>Abstract</h2> Visual object tracking methods depend upon deep networks that can hardly meet real-time processing requirements on mobile platforms with limited computing resources. In this work, we propose a real-time object tracking framework by enhancing a lightweight feature pyramid network with Transformer architecture to construct a robust target-specific appearance model efficiently. We further introduce the pooling attention module to avoid the computation and memory intensity while fusing pyramid features with the Transformer. The optimized tracker operates over 45 Hz on a single CPU, allowing researchers to deploy it on any mobile device with limited power resources.
Highlights
Visual Object Tracking (VOT) has attracted increasing attention in recent years, given its applications in several fields, including path planning [1], visual surveillance [2] and border security [3]
While extensive achievements have been made towards powerful object tracking methods, most of those trackers employ deep networks, complex structures, or online update mechanisms and require GPU acceleration to achieve real-time processing
We propose a novel object tracker optimized for mobile devices, which achieves high-performance and real-time speed using only a CPU-component
Summary
Daitao Xing a,∗, Nikolaos Evangeliou b, Athanasios Tsoukalas b, Anthony Tzes c a New York University, USA b New York University Abu Dhabi, United Arab Emirates c Center for Artificial Intelligence and Robotics, New York University Abu Dhabi, United Arab Emirates
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