DocumentCode :
3696224
Title :
The Importance of Feature Representation for Visual Tracking Systems with Discriminative Methods
Author :
Jialin Lu;Hongxin Li
Author_Institution :
Dept. of Commun. &
Volume :
2
fYear :
2015
Firstpage :
190
Lastpage :
193
Abstract :
Visual tracking has been a challenging problem in the field of computer vision due to a variety of appearance changes of target object, while it is a widely explored area, with many applications in human-computer interaction, surveillance and robotics. Recently thanks to a great progress made by machine learning researchers, more and more sophisticated techniques have been applied to visual tracking. In that case, the tracking task is easily translated into a binary classification problem. Based on this framework, In this paper we investigate the influence of different feature representations on the performance of a tracker by designing controlled experiments. Finally, we find that feature representation plays a crucial role in a visual tracking system. Additionally, although the complex model learning algorithm is the focus of many attentions and studies, our experiments indicate that a good feature representation is much more important than using a complex classification algorithm in a visual tracking system. We believe that our work will provide a fresh perspective for the research of visual tracking which can dramatically improve tracking performances.
Keywords :
"Feature extraction","Kernel","Image color analysis","Visualization","Target tracking","Training","Gray-scale"
Publisher :
ieee
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2015 7th International Conference on
Print_ISBN :
978-1-4799-8645-3
Type :
conf
DOI :
10.1109/IHMSC.2015.160
Filename :
7334948
Link To Document :
بازگشت