@Article{2018.100000056,
AUTHOR = {Yong‐Hwan Lee, Hyochang Ahn, Hyo‐Beom Ahn, Sun‐Young Lee},
TITLE = {Visual Object Detection and Tracking Using Analytical Learning Approach  of Validity Level},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {1},
PAGES = {205--215},
URL = {http://www.techscience.com/iasc/v25n1/39647},
ISSN = {2326-005X},
ABSTRACT = {Object tracking plays an important role in many vision applications. This paper 
proposes a novel and robust object detection and tracking method to localize 
and track a visual object in video stream. The proposed method is consisted of 
three modules; object detection, tracking and learning. Detection module finds 
and localizes all apparent objects, corrects the tracker if necessary. Tracking 
module follows the interest object by every frame of sequences. Learning 
module estimates a detecting error, and updates its value of credibility level. 
With a validity level where the tracking is failed on tracing the learned object, 
detection module finds again the desired object. The experimental results show 
that the proposed approach is more robust in appearance changes, viewpoint 
changes, and rotation of the object, compared to the traditional method. The 
proposed method can track the interest object accurately in various 
environments.},
DOI = {10.31209/2018.100000056}
}