Paper Info
Title
Fusion of Thermal and Visible Imagery for Effective Detection and Tracking of Salient Objects in Videos.
Abstract
In this paper, we present an efficient approach to detect and track salient objects from videos. In general, colored visible image in red-green-blue (RGB) has better distinguishability in human visual perception, yet it suffers from the effect of illumination noise and shadows. On the contrary, thermal image is less sensitive to these noise effects though its distinguishability varies according to environmental settings. To this end, fusion of these two modalities provides an effective solution to tackle this problem. First, a background model is extracted followed by background-subtraction for foreground detection in visible images. Meanwhile, adaptively thresholding is applied for foreground detection in thermal domain as human objects tend to be of higher temperature thus brighter than the background. To deal with cases of occlusion, prediction based forward tracking and backward tracking are employed to identify separate objects even the foreground detection fails. The proposed method is evaluated on OTCBVS, a publicly available color-thermal benchmark dataset. Promising results have shown that the proposed fusion based approach can successfully detect and track multiple human objects.
Year
Venue
Field
2016
PCM
Computer vision,Colored,Thermal,Pattern recognition,Image fusion,Computer science,Salient objects,Fusion,Foreground detection,Artificial intelligence,RGB color model,Thresholding
DocType
Citations 
PageRank 
Conference
1
0.37
References 
Authors
10
6
Name
Order
Citations
PageRank
Yijun Yan111.39
Jinchang Ren2114488.54
Huimin Zhao320623.43
jiangbin zheng4775.68
Ezrinda Mohd Zaihidee510.37
John J. Soraghan616634.16