TY - JOUR
T1 - Detecting and tracking dim small targets in infrared image sequences under complex backgrounds
AU - Li, Ying
AU - Liang, Shi
AU - Bai, Bendu
AU - Feng, David
PY - 2014/8
Y1 - 2014/8
N2 - This paper presents a unified framework for automatically detecting and tracking dim small targets in infrared (IR) image sequence under complex backgrounds. Firstly, the variance weighted information entropy (variance WIE) followed by a region growing technique is introduced to segment the candidate targets in a single-frame IR image after background suppression. Then the pipeline filter is used to verify the real targets. The position and the size of the detected target are then obtained to initialize the tracking algorithm. Secondly, we adopt an improved local binary pattern (LBP) scheme to represent the target texture feature and propose a joint gray-texture histogram method for a more distinctive and effective target representation. Finally, target tracking is accomplished by using the mean shift algorithm. Experimental results indicate that the proposed method can effectively detect the dim small targets under complex backgrounds and has better tracking performance compared with the gray histogram based tracking methods such as the mean shift and the particle filtering.
AB - This paper presents a unified framework for automatically detecting and tracking dim small targets in infrared (IR) image sequence under complex backgrounds. Firstly, the variance weighted information entropy (variance WIE) followed by a region growing technique is introduced to segment the candidate targets in a single-frame IR image after background suppression. Then the pipeline filter is used to verify the real targets. The position and the size of the detected target are then obtained to initialize the tracking algorithm. Secondly, we adopt an improved local binary pattern (LBP) scheme to represent the target texture feature and propose a joint gray-texture histogram method for a more distinctive and effective target representation. Finally, target tracking is accomplished by using the mean shift algorithm. Experimental results indicate that the proposed method can effectively detect the dim small targets under complex backgrounds and has better tracking performance compared with the gray histogram based tracking methods such as the mean shift and the particle filtering.
KW - Dim small target detection and tracking
KW - Local binary pattern (LBP)
KW - Mean shift
KW - Variance weighted information entropy (variance WIE)
UR - http://www.scopus.com/inward/record.url?scp=84904389586&partnerID=8YFLogxK
U2 - 10.1007/s11042-012-1258-y
DO - 10.1007/s11042-012-1258-y
M3 - 文章
AN - SCOPUS:84904389586
SN - 1380-7501
VL - 71
SP - 1179
EP - 1199
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 3
ER -