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

Abstract

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 the 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. The detection position was repositioned according to the estimated speed of target by an optical flow method, and the alternate template was stored with a template update mechanism, which is all computed at the edge. Experimental results on large-scale public benchmark data sets showed the effectiveness of the proposed method in comparison with state-of-the-art methods.