Earticle

초록

영어

Tracking multiple objects in real-time videos represents a challenging area in the era of computer vision. This paper proposes a new method to track the multiple objects under different environment conditions such as rotation, illumination, blurred, occlusion, and many others. In addition, the kinect color depth image processing is used to estimate the distance of the objects. The tracking of multiple objects is formulated as classification task which competitively use the object features in the different video frames of the video sequences. To obtain the optimal configuration of feature classification, a neural network based framework is presented to make a global influence based on winner pixel estimation between the video frames. The objects are tracked efficiently in less time as compared with SIFT techniques and distance of objects is calculated with kinect based depth image processing. Experimental results are given for real-time scenes, and many experiments are conducted to examine the performance of the proposed approach. The proposed method resulted into efficient tracking of multiple objects in various conditions including rotation, scaling, occlusion, etc. The distance of multiple tracked objects is estimated using the kinect depth processing.

목차

Abstract
 1. Introduction
 2. Self-Organizing Map and Kinect Color Depth Processing
  2.1. Self-Organizing Map
  2.2. Kinect Color Depth Processing
 3. Proposed Multiple Object Tracking Algorithm
  3.1. Target Objects Feature Vector Generation
  3.2. Multiple Object Tracking with Improved Feature Matching
 4. Results and Discussion
 5. Conclusion
 References

키워드

저자

• Kajal Sharma [ Flat No. 301, Building 206, Piorville Apartment, 24 Namyang-Dong, Seongsangu, Changwon, Korea, Independent Researcher ]

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