Earticle

초록

영어

Video tracking is one of the most active research topics recently. Tracking of objects and humans has a very wide set of applications such as teleconferencing, surveillance, and security. We propose a new tracker to enhance the tracking process by making use of SURF descriptor and Particle filter. SURF is one of the fastest descriptors which generates a set of interesting points which are invariant to various image deformations (scaling, rotation, illumination) and robust against occlusion conditions during tracking. Particle filter is one of the commonly used methods in video tracking to solve non-linear and non-Gaussian problems. Particle filter generates a random set of points called particles or samples for any target to be used for tracking through the process of the algorithm. But the fact that the initial particles are chosen randomly causes degradation in efficiency and reliability of the tracking process. It is possible to lose the tracked target at any frame if any change happened in the scene. Previous researches proposed an integration of Particle algorithm and scale invariant feature transform (SIFT) descriptor to overcome potential problems. SIFT is a predecessor of SURF and shares the same characteristics except that SURF is much faster. A comparative study was held between the traditional particle filter, SIFT-Particle tracker and the proposed tracker. The proposed SURF-Particle tracker proved to be more efficient, reliable and accurate than traditional particle filter and SIFT-Particle tracker. The idea of the proposed tracker is to use the discriminative interest points generated by the SURF descriptor as the initial particles/ samples to be fed into particle filter instead of choosing these particles randomly as done in traditional simple particle filter. Experimental results using the Actions as Space-Time Shapes Dataset of the Weizmann Institute of Science proved the correctness of the proposed idea and showed improved efficiency and accuracy resulted from using our proposed tracker over traditional simple particle filter and SIFT-Particle tracker. It also proved to be faster than SIFT-Particle.

목차

Abstract
 1. Introduction
 2. Background
  2.1 Particle Filtering
  2.2 Speeded Up Robust Features (SURF)
  2.3 Scale- Invariant Feature Transform (SIFT)
 3. SURF-Particle Tracker
 4. Experimental Results
 5. Conclusions and Future Work
 References

키워드

저자

• H. Kandil [ Information Technology Department, Faculty of Computer and Information Sciences, Mansoura University ]
• A. Atwan [ Information Technology Department, Faculty of Computer and Information Sciences, Mansoura University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605