Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82416
Title: Visual Tracking via Random Partition Image Hashing
Authors: Guan, Mingyang
Wen, Changyun
Lim, Kwang-Yong
Shan, Mao
Tan, Paul
Ng, Cheng-Leong
Zou, Ying
Keywords: binary codes
random processes
Issue Date: 2016
Source: Guan, M., Wen, C., Lim, K.-Y., Shan, M., Tan, P., Ng, C.-L., et al. (2016). Visual tracking via random partition image hashing. 2016 14th International Conference on Control, Automation, Robotics & Vision (ICARCV), 1-6.
Conference: 2016 14th International Conference on Control, Automation, Robotics & Vision (ICARCV)
Abstract: 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.
URI: https://hdl.handle.net/10356/82416
http://hdl.handle.net/10220/42305
DOI: 10.1109/ICARCV.2016.7838673
Schools: School of Electrical and Electronic Engineering 
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://doi.org/10.1109/ICARCV.2016.7838673].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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