Visual Tracking via Random Partition Image Hashing

In this paper, we propose a discriminative and robust appearance model based on features extracted from a random partition image hashing algorithm to account for severe occlusion and disappearance. We divide the original image into multiple sub-blocks with random positions and scales. Hash functions...

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書目詳細資料
Main Authors: Guan, Mingyang, Wen, Changyun, Lim, Kwang-Yong, Shan, Mao, Tan, Paul, Ng, Cheng-Leong, Zou, Ying
其他作者: School of Electrical and Electronic Engineering
格式: Conference or Workshop Item
語言:English
出版: 2017
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在線閱讀:https://hdl.handle.net/10356/82416
http://hdl.handle.net/10220/42305
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總結:In this paper, we propose a discriminative and robust appearance model based on features extracted from a random partition image hashing algorithm to account for severe occlusion and disappearance. We divide the original image into multiple sub-blocks with random positions and scales. Hash functions are used to map blocks into compact binary codes, with which more effective target matching can be achieved. The tracking task is then formulated by producing a confidence map for the target and background, and obtaining the best samples using maximum a posteriori estimate. Experimental results demonstrate that our tracker can achieve more accurate tracking results in situations of occlusion, out-of-view, and violent motion blur when compared with most of state-of-the-art competing algorithms. Besides, the proposed tracking algorithm is able to run in real time.