.png){kind=icon}
Return
Quad binary pattern and its application in mean-shift tracking
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2015.11.130.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a new local texture descriptor, called quad binary pattern (QBP). Compared with local binary pattern (LBP), the QBP is with stronger robustness for feature extraction under complex scene (e.g., luminance change, similar target and background color) and with lower computational complexity. To demonstrate its effectiveness, the proposed QBP is further applied on the mean-shift tracking, in which a joint color-QBP model is developed to effectively represent the color and texture characteristics of the target region. Extensive simulation results have demonstrated that the proposed algorithm is able to improve the tracking speed and accuracy, compared with the standard mean-shift tracking and joint color-LBP model based mean-shift tracking. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Target tracking
Quad binary pattern
Mean-shift tracking
Joint color-QBP model
Feature extraction
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
Organization
Cited Papers
ShareSavePLBP: An effective local binary patterns texture descriptor with pyramid representation.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveFacial expression recognition based on improved local binary pattern and class-regularized locality preserving projectionSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
ShareSaveShareSaveShareSaveShareSave
View more