In-Vehicle Edge System for Real-Time Dashcam Video Analysis

TL;DR

This paper introduces DEVA, a distributed edge-based system that analyzes real-time dashcam videos on in-vehicle devices to detect hazards and distracted driving, enhancing safety without relying on external infrastructure.

Contribution

The paper presents a novel distributed edge system for real-time dashcam video analysis that dynamically manages resources and processes multiple video streams efficiently.

Findings

Processes two dashcam streams at 22-30 FPS with 200 ms latency

Uses resource-aware distribution of analysis tasks among devices

Achieves real-time hazard detection in vehicle environments

Abstract

Modern vehicles equip dashcams that primarily collect visual evidence for traffic accidents. However, most of the video data collected by dashcams that is not related to traffic accidents is discarded without any use. In this paper, we present a use case for dashcam videos that aims to improve driving safety. By analyzing the real-time videos captured by dashcams, we can detect driving hazards and driver distractedness to alert the driver immediately. To that end, we design and implement a Distributed Edge-based dashcam Video Analytics system (DEVA), that analyzes dashcam videos using personal edge (mobile) devices in a vehicle. DEVA consolidates available in-vehicle edge devices to maintain the resource pool, distributes video frames for analysis to devices considering resource availability in each device, and dynamically adjusts frame rates of dashcams to control the overall workloads. The entire video analytics task is divided into multiple independent phases and executed in a pipelined manner to improve the overall frame processing throughput. We implement DEVA in an Android app and also develop a dashcam emulation app to be used in vehicles that are not equipped with dashcams. Experimental results using the apps and commercial smartphones show that DEVA can process real-time videos from two dashcams with frame rates of around 22~30 FPS per camera within 200 ms of latency, using three high-end devices.