Open AccessRobust Vision-based Multiple Moving Object Detection and Tracking from Video Sequences
Author(s) -
Othman O. Khalifa,
Norun Abdul Malek,
Kazi Istiaque Ahmed
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v10.i2.pp817-826
Subject(s) - background subtraction , computer vision , artificial intelligence , computer science , video tracking , object detection , frame (networking) , feature (linguistics) , tracking (education) , object (grammar) , viola–jones object detection framework , object class detection , pixel , track (disk drive) , pattern recognition (psychology) , face detection , facial recognition system , psychology , telecommunications , pedagogy , linguistics , philosophy , operating system
Detection of Moving Objects and Tracking is one of the most concerned issue and is being vastly used at home, business and modern applications. It is used to identify and track of an entity in a significant way. This paper illustrates the way to detect multiple objects using background subtraction methods and extract each object features by using Speed-Up Robust Feature algorithm and track the features through k-Nearest Neighbor processing from different surveillance videos sequentially. In the detection of object of each frame, pixel difference is calculated with respect to the reference background frame for the detection of an object which is only suitable for any ideal static condition with the consideration of lights from the environment. Thus, this method will detect the complete object and the extracted feature will be carried out for the tracking of the object in the multiple videos by one by one video. It is expected that this proposed method can commendably abolish the impact of the changing of lights