Research Repository

Object Tracking in Vary Lighting Conditions for Fog based Intelligent Surveillance of Public Spaces

Liu, G and Liu, S and Muhammad, K and Sangaiah, AK and Doctor, F (2018) 'Object Tracking in Vary Lighting Conditions for Fog based Intelligent Surveillance of Public Spaces.' IEEE Access, 6. pp. 29283-29296. ISSN 2169-3536

08357547.pdf - Published Version

Download (2MB) | Preview


With rapid development of computer vision and artificial intelligence, cities are becoming more and more intelligent. Recently, since intelligent surveillance was applied in all kind of smart city services, object tracking attracted more attention. However, two serious problems blocked development of visual tracking in real applications. The first problem is its lower performance under intense illumination variation while the second issue is its slow speed. This paper addressed these two problems by proposing a correlation filter based tracker. Fog computing platform was deployed to accelerate the proposed tracking approach. The tracker was constructed by multiple positions' detections and alternate templates (MPAT). The detection position was repositioned according to the estimated speed of target by optical flow method, and the alternate template was stored with a template update mechanism, which were all computed at the edge. Experimental results on large-scale public benchmark datasets showed the effectiveness of the proposed method in comparison with state-of-the-art methods.

Item Type: Article
Uncontrolled Keywords: Correlation; Edge computing; Filtering algorithms; Lighting; Object tracking; Surveillance; Target tracking; fog computing; illumination variation; intelligent surveillance; smart city
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 23 May 2018 13:32
Last Modified: 15 Jan 2022 01:24

Actions (login required)

View Item View Item