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Distributed Tracking and Verifying: A Real-Time and High-Accuracy Visual Tracking Edge Computing Framework for Internet of Things

Published: 07 August 2024 Publication History

Abstract

We observe that accurate and fast tracking in Internet of Things (IoT) devices is still a challenging problem. Several deep learning models have emerged which provide higher accuracy scores in object detection and tracking, however, due to their computationally expensive nature they are not useful in enabling real-time tracking at IoT devices. Correlation filters have emerged to show better speed in real-time tracking and provide good tracking results in cases of occlusion, rotation, illumination and other distractions. To get better speed as well as accuracy we use combination of correlation filter and deep learning methods. We propose a distributed tracking and verifying (DTAV) framework. Specifically, we run two object tracking algorithms, one on the client and another on the server. The algorithm run on the client is referred to as the Tracker, which is based on correlation filter and runs easily in real-time. The server hosts the verifier algorithm which performs high accuracy verification. Thus, while the client performs fast object tracking, the server's tracking algorithm verifies the output and corrects the server whenever required to maintain the accuracy of the model. We present our edge computing-based framework and discuss the motivation, system setup and series of experiments performed for the framework and present our experimental results. DTAV achieved 7.78% improvement on accuracy and 15% improvement in FPS.

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cover image ACM Conferences
SEC '23: Proceedings of the Eighth ACM/IEEE Symposium on Edge Computing
December 2023
405 pages
ISBN:9798400701238
DOI:10.1145/3583740
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 07 August 2024

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  1. edge computing
  2. distributed inference
  3. visual object tracking for IoT

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SEC '23
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SEC '23: Eighth ACM/IEEE Symposium on Edge Computing
December 6 - 9, 2023
DE, Wilmington, USA

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