Open AccessReal‐time multi‐class moving target tracking and recognition
Author(s) -
Zhang QingNian,
Sun YaDong,
Yang Jie,
Liu HaiBo
Publication year - 2016
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2014.0226
Subject(s) - artificial intelligence , computer science , video tracking , class (philosophy) , tracking (education) , computer vision , cluster analysis , mixture model , tracking system , cognitive neuroscience of visual object recognition , gaussian , pattern recognition (psychology) , object (grammar) , kalman filter , psychology , pedagogy , physics , quantum mechanics
The existing tracking and recognition methods concentrate mainly on single‐class targets; however, systems for traffic management or intelligent transport often require multi‐class target tracking and recognition in real time. This study proposes an effective multi‐class moving target recognition method that is based on Gaussian mixture part‐based model, which accurately locates objects of interest and recognises their corresponding categories. The method is multi‐threaded and combines soft clustering approach with multiple mixture part based models to provide stable multi‐class target tracking and recognition in video sequences. The highlight of the method is its ability to recognise multi‐class moving targets and to count their numbers in the video sequence captured by a stationary camera with fixed focal length. Another contribution of this study is that an extended part based model is developed for object recognition in real‐world environments, which can improve the overall system performance, lower time costs, and better meet the actual demand of a video system. Experimental results show that the proposed method is viable in real‐time multi‐class moving target tracking and recognition.