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An Embedded Computer-Vision System for Multi-Object Detection in Traffic Surveillance | IEEE Journals & Magazine | IEEE Xplore

An Embedded Computer-Vision System for Multi-Object Detection in Traffic Surveillance


Abstract:

Intelligent traffic systems for traffic surveillance and monitoring have become a topic of great interest to some cities in the world. Generally, the existing traffic sur...Show More

Abstract:

Intelligent traffic systems for traffic surveillance and monitoring have become a topic of great interest to some cities in the world. Generally, the existing traffic surveillance systems are made up of costly equipment with complicated operational procedures and have difficulties with congestion, occlusion, and lighting night/day and day/night transitions. In this paper, we propose an embedded system for traffic surveillance that can be utilized under these challenging conditions. This system analyses traffic and particularly focuses on the problem of detecting and categorizing traffic objects in several traffic scenarios. Moreover, it contains a robust detector produced by an original specialization framework. The proposed specialization framework utilizes a generic deep detector so as to improve the detection accuracy in a specific traffic scenario. The experiments demonstrate that the proposed specialization framework presents encouraging results for multi-traffic object detection and outperforms the state-of-the-art specialization frameworks on several public traffic datasets.
Published in: IEEE Transactions on Intelligent Transportation Systems ( Volume: 20, Issue: 11, November 2019)
Page(s): 4006 - 4018
Date of Publication: 27 November 2018

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I. Introduction

Vision-based traffic vehicle surveillance is still a complicated part within any traffic surveillance system due to several factors like illumination variations, camera calibration and daytime conditions. Accordingly, the performance requirements are not left in prototype works at research labs anymore but they are exposed to real world problems. This demand makes the task of building such a system highly challenging, especially when accuracy and speed are required.

References

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