Paper Info
Title
A Low-Latency Object Detection Algorithm for the Edge Devices of IoV Systems
Abstract
The emergence of edge computing (EC) and intelligent vision-based driver assistance system is of great significance for the prospective development of Internet of Vehicle (IoV). The additional computation capability and extensive network coverage provides energy-limited smart devices with more opportunities to enable IoV system for time-sensitive applications. However, when implemented in a vision-based driver assistance system, the transmission of a large amount of redundant data not only causes delay but also severely compromises the accuracy of object detection. In this paper, an improved object detection algorithm based on video key-frame for latency reduction on edge IoV system is proposed. It can significantly improve latency reduction performance at the expense of small detection accuracy. In our proposal, we adopt an important coefficient and frame similarity comparison algorithm to filter redundant frames and achieve key frames for object detection. Then an improved Haar-like feature based classification algorithm is used for object detection under the edge computation model. Finally, a scalable cluster object detection system is implemented as a practical EC case to verify our proposal, and extensive simulations confirm the superiority of the proposed scheme over regular schemes. It can speed up about 84 times with 40% of the similar frames filtered in comparison.
Year
DOI
Venue
2020
10.1109/TVT.2020.3008265
IEEE Transactions on Vehicular Technology
Keywords
DocType
Volume
Edge computing,internet of vehicle,object detection,frame similarity compression,latency reduction
Journal
69
Issue
ISSN
Citations 
10
0018-9545
6
PageRank 
References 
Authors
0.47
0
4
Name
Order
Citations
PageRank
Cheng Dai1165.03
Xingang Liu2446.71
Weiting Chen360.47
Chin-Feng Lai497374.85