Distributed Edge-based Video Analytics on the Move

TL;DR

This paper presents EdgeDashAnalytics, a system enabling near real-time, in-situ video analytics on mobile devices for dash cam videos, addressing the challenge of resource constraints and mobility in real-time applications.

Contribution

The paper introduces EDA, an edge-based system that processes dash cam videos on mobile devices with optimizations for real-time analysis, demonstrating feasibility and efficiency.

Findings

Achieves near real-time analysis with acceptable accuracy loss.

Demonstrates feasibility on resource-constrained mobile devices.

Shows reduced energy consumption and turnaround time.

Abstract

In recent years, we have witnessed an explosive growth of data. Much of this data is video data generated by security cameras, smartphones, and dash cams. The timely analysis of such data is of great practical importance for many emerging applications, such as real-time facial recognition and object detection. In this study, we address the problem of real-time in-situ video analytics with dash cam videos and present EdgeDashAnalytics (EDA), an edge-based system that enables near real-time video analytics using a local network of mobile devices. In particular, it simultaneously processes videos produced by two dash cams of different angles with one or more mobile devices on the move in a near real-time manner. One camera faces outward to capture the view in front of the vehicle, while the other camera faces inward to capture the driver. The outer videos are analysed to detect potential driving hazards, while the inner videos are used to identify driver distractedness. EDA achieves near real-time video analytics using resource-constrained, transient mobile devices by devising and incorporating several optimisations, with a tolerable loss in accuracy. We have implemented EDA as an Android app and evaluated it using two dash cams and several heterogeneous mobile devices with the BDD100K dash cam video dataset (arXiv:1805.04687 [cs.CV]) and the DMD driver monitoring dataset (arXiv:2008.12085 [cs.CV]). Experiment results demonstrate the feasibility of real-time video analytics in terms of turnaround time and energy consumption (or battery usage), using resource-constrained mobile devices on the move.