Kim, Chansu Song, Donghui Kim, Chang-Su Park, Sung-Kee 2024-01-19T20:30:49Z 2024-01-19T20:30:49Z 2021-09-02 2019-04 0020-0255 https://pubs.kist.re.kr/handle/201004/120146 We propose an object tracking method under large motion in image sequences. Dense sampling and particle filtering have been widely applied to cope with this problem; however, the former is computationally expensive, and the latter is sensitive to local minima. By introducing a novel search method based on coarse-to-fine strategy and image superpixels, we try to solve both drawbacks. In the coarse step, we first extract superpixels associated with a target object on the entire search region by using a simple generative appearance model. In the fine step, we perform a sampling and similarity measurement process within the selected superpixels to find the most accurate location of the target object, also suggest a way to use both a discriminative appearance model and a sophisticated generative appearance model simultaneously. Extensive experiments on popular benchmark dataset demonstrate that the proposed method outperforms other competitive approaches, and also show better results in challenging scenarios such as occlusion, deformation, out-of-view, and in-plane/out-of-plane rotation. (C) 2019 Elsevier Inc. All rights reserved. English ELSEVIER SCIENCE INC VISUAL TRACKING Object tracking under large motion: Combining coarse-to-fine search with superpixels Article 10.1016/j.ins.2018.12.042 1 INFORMATION SCIENCES, v.480, pp.194 - 210 INFORMATION SCIENCES 480 194 210 scie scopus 000459644700012 2-s2.0-85059000542 Computer Science, Information Systems Computer Science Article VISUAL TRACKING Object tracking Large motion Coarse-to-fine search Superpixel Hybrid appearance model