Open AccessK ‐means based multiple objects tracking with long‐term occlusion handling
Author(s) -
Shehzad Muhammad Imran,
Shah Yasir A.,
Mehmood Zahid,
Malik Abdul Waheed,
Azmat Shoaib
Publication year - 2017
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2016.0156
Subject(s) - computer science , artificial intelligence , computer vision , tracking (education) , term (time) , occlusion , object (grammar) , computation , video tracking , measure (data warehouse) , active appearance model , scheme (mathematics) , state (computer science) , pattern recognition (psychology) , image (mathematics) , data mining , algorithm , mathematics , medicine , psychology , mathematical analysis , pedagogy , physics , quantum mechanics , cardiology
This study presents a novel multiple objects tracking (MOT) approach that models object's appearance based on K ‐means, while introducing a new statistical measure for association of objects after occlusion. The proposed method is tested on several standard datasets dealing complex situations in both indoor and outdoor environments. The experimental results show that the proposed model successfully tracks multiple objects in the presence of occlusion with high accuracy. Moreover, the presented work has the capability to deal long term and complete occlusion without any prior training of the shape and motion model of the objects. Accuracy of the proposed method is comparable with that of the existing state‐of‐the‐art techniques as it successfully deals with all MOT cases in the standard datasets. Most importantly, the proposed method is cost effective in terms of memory and/or computation as compared with that of the existing state‐of‐the‐art techniques. These traits make the proposed system very useful for real‐time embedded video surveillance platforms especially those that have low memory/compute resources.