Comparison of SIFT and ASIFT based filters for better recognition and tracking in a remote scene environment

Haris Masood; Saad Rehman; Qaiser Chaudry; Farhan Riaz; Ali Hassan; Umar Farooq

doi:10.1117/12.2305266

30 April 2018 Comparison of SIFT and ASIFT based filters for better recognition and tracking in a remote scene environment

Haris Masood, Saad Rehman, Qaiser Chaudry, Farhan Riaz, Ali Hassan, Umar Farooq

Author Affiliations +

Proceedings Volume 10649, Pattern Recognition and Tracking XXIX; 106490Z (2018) https://doi.org/10.1117/12.2305266
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

In a remote scene environment consisting of multiple objects and miscellaneous scenarios, detecting an object of interest is a troublesome task especially while tracking the object over successive frames. Numerous methods have been proposed over the years for efficient detection of object of interest in a remote scene environment while in he meanwhile discarding all those which aren’t of interest and thus considered as noise. It is still one of the most actively researched areas in the field of image processing and computer vision. In this paper, a method is proposed which will not only detect a fixed shape object in a remote scene environment but it will also track it over successive frames. However, an additional methodology is also proposed which will detect the object in case of change of viewing angles e.g. scenario’s like rotation of object, zooming etc. First, Scale Invariant Feature Transform (SIFT) will be presented which will provide invariance up to four different parameters i.e. rotation, translation and zoom. In the second phase, ASIFT will be used which will provide invariance up to six different parameters i.e. translation, rotation, zoom and camera axis orientations. After both algorithms are presented, a detailed comparison between both is presented. Detection of object is performed with the help of both SIFT and ASIFT and then comparison is made based on feature points. Finally, Tracking is performed based on Proximal Gradient Particle filters which will further strengthen the comparison between SIFT and ASIFT once the object that needs to be tracked changes its course of motion or zoom. Experimental results will show which one of the two filters is more efficient.

Conference Presentation

Citation Download Citation

Haris Masood, Saad Rehman, Qaiser Chaudry, Farhan Riaz, Ali Hassan, and Umar Farooq "Comparison of SIFT and ASIFT based filters for better recognition and tracking in a remote scene environment", Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 106490Z (30 April 2018); https://doi.org/10.1117/12.2305266

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available