Mobile Object Tracking with Siamese Neural Network

Abstract

Visual object tracking is one of the most fundamental research topics in computer vision that aims to obtain the target object’s location in a video sequence given the object’s initial state in the first video frame. The recent advance of deep neural networks, specifically Siamese networks, has led to significant progress in visual object tracking. Despite being accurate and achieving high results on academic benchmarks, current state-of-the-art approaches are compute-intensive and have a large memory footprint that cannot satisfy the strict performance requirements of realworld applications. This work focuses on designing a novel lightweight framework for resource-efficient and accurate visual object tracking. Additionally, we introduce a new tracker efficiency benchmark and protocol where efficiency is defined in terms of both energy consumption and execution speed on edge devices.