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POSE-VIWEPOINT ADAPTIVE OBJECT TRACKING VIA ONLINE LEARNING APPROACH
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 Title & Authors
POSE-VIWEPOINT ADAPTIVE OBJECT TRACKING VIA ONLINE LEARNING APPROACH
Mariappan, Vinayagam; Kim, Hyung-O; Lee, Minwoo; Cho, Juphil; Cha, Jaesang;
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 Abstract
In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.
 Keywords
Object Tracking;Features Extraction;Effective Tracking Algorithm;Computer Vision;Target Detection and Tracking;Online Learning;TLD;Random Forest;Naive Bayes;long-Term Object Tracking;Adaptive Appearance Model;
 Language
English
 Cited by
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