Abstract
Visual tracking is a popular topic in computer vision due to its importance in surveillance, action recognition and event detection. The feature to describe the visual object is an essential element of the tracking model. But there does not exist such kind of feature to handle all situations. Based on this fact, researchers propose the fusion technique to capture robust representation of the object by integrating different features. However, general fusion methods are hard to be applied to tracking algorithm due to the reason of processing speed and online update. To solve this problem, an effective fusion-based hashing method is proposed. The hashing method fuses different features to generate compact binary feature, which could be efficiently processed. In addition, 2D manner and online update model are used to improve the tracker’s performance. Experimental results demonstrate that our tracker out-performs the state-of-the-art trackers in tested sequences.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
