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http://dx.doi.org/10.14372/IEMEK.2021.16.6.323

Reinforced Feature of Dynamic Search Area for the Discriminative Model Prediction Tracker based on Multi-domain Dataset  

Lee, Jun Ha (Korea Institute of Industrial Technology, Kyungpook National University)
Won, Hong-In (Korea Institute of Industrial Technology)
Kim, Byeong Hak (Korea Institute of Industrial Technology)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.16, no.6, 2021 , pp. 323-330 More about this Journal
Abstract
Visual object tracking is a challenging area of study in the field of computer vision due to many difficult problems, including a fast variation of target shape, occlusion, and arbitrary ground truth object designation. In this paper, we focus on the reinforced feature of the dynamic search area to get better performance than conventional discriminative model prediction trackers on the condition when the accuracy deteriorates since low feature discrimination. We propose a reinforced input feature method shown like the spotlight effect on the dynamic search area of the target tracking. This method can be used to improve performances for deep learning based discriminative model prediction tracker, also various types of trackers which are used to infer the center of the target based on the visual object tracking. The proposed method shows the improved tracking performance than the baseline trackers, achieving a relative gain of 38% quantitative improvement from 0.433 to 0.601 F-score at the visual object tracking evaluation.
Keywords
Visual object tracking; Deep learning; Reinforced feature; Discriminative model prediction;
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