Open AccessMulti Target Tracking Access with Data Association in Distributed Camera Networks
Author(s) -
Azmira Krishna,
Zeelan Cmak,
Rahul Pradeep,
Soumya Ranjan Nayak,
S. Sivakumar,
Zeelan Basha,
Raj Savarapu,
Soumya Ranjan Nayak,
S. Sivakumar
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1063.0982s1119
Subject(s) - computer science , artificial intelligence , computer vision , tracking (education) , association (psychology) , homography , camera auto calibration , video tracking , data association , smart camera , object (grammar) , position (finance) , camera resectioning , mathematics , psychology , pedagogy , philosophy , statistics , projective test , epistemology , projective space , probabilistic logic , finance , economics
Data Association in Distributed Camera Network is a new method to analyse the large volume of video information in camera networking. It is an important step in multi camera multi target tracking. Distributed processing is a new paradigm to analyse the videos in camera network and each camera acts on its own and all cameras cooperatively work together to achieve a common goal, In this paper, we have addresses the problem of Distributed Data Association(DDA) to obtain the feet position of the object. These positions are shared with its immediate neighbours and find local matches using homography. By propagating these local matches across the network in order to obtain the global associations. In this proposed method DDA is less complex and improves the high accuracy compared to the centralized methods (STSPIE, EMTIC, JPDAEKCF, CSPIF, and CEIF).