Authors:
Tuan Nguyen
and
Tony Pridmore
Affiliation:
University of Nottingham, United Kingdom
Keyword(s):
Adaptive tracking, Appearance Model, Motion Model.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
Abstract:
We introduce a unified tracker, named as a feature based multiple model tracker (FMM), which adapts to changes in target appearance by combining two popular generative models: templates and histograms, maintaining multiple instances of each in an appearance pool, and enhances prediction by utilising multiple linear searches. These search directions are sparse estimates of motion direction derived from local features stored in a feature pool. Given only an initial template representation of the target, the proposed tracker can learn appearance changes in a supervised manner and generate appropriate target motions without knowing the target movement in advance. During tracking, it automatically switches between models in response to variations in target appearance, exploiting the strengths of each model component. New models are added, automatically, as necessary. The effectiveness of the approach is demonstrated using a variety of challenging video sequences. Results show that this fr
amework outperforms existing appearance based tracking frameworks.
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