Author:
Ali Ahmed
Affiliation:
The university of Tokushima, Japan
Keyword(s):
Change Detection, Object Tracking, Kalman filter, Mean shift Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Tracking of People and Surveillance
Abstract:
Object detection in videos involves verifying the presence of an object in image sequences and possibly locating it precisely for recognition. Object tracking is to monitor an object's spatial and temporal changes during a video sequence, including its presence, position, size, shape, etc. These two processes are closely related because tracking usually starts with detecting objects, while detecting an object repeatedly in subsequent image sequence is often necessary to help and verify tracking. In this paper, a novel approach is being presented for detecting and tracking object. It includes combination of Kalman filter and fast mean shift algorithm. Kalman prediction is measurement follower. It may be misled by wrong measurement. In order to cater it, fast mean shift algorithm is used. It is used to locate densities extrema, which gives clue that whether Kalman prediction is right or it is misled by wrong measurement. In case of wrong prediction, it is corrected with the help of de
nsities extrema in the scene. The proposed approach has the robust ability to track the moving object in the consecutive frames under some kinds of difficulties such as rapid appearance changes caused by image noise, illumination changes, and cluttered background.
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