淡江大學機構典藏:Item 987654321/118378
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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/118378


    Title: Reptile Meta-Tracking
    Authors: Jhang, Shang-Jhih;Tsai, Chi-Yi
    Keywords: Generic object tracking;visual tracking;deep learning;few-shot learning;Reptile meta-learning
    Date: 2019
    Issue Date: 2020-03-21 12:11:21 (UTC+8)
    Abstract: Generic object tracking (GOT) is one of the main topics in computer vision for many years. The goal of GOT is to recognize and locate a specific object in the form of bounding box throughout a sequence of images. Moreover, GOT also requires algorithms to locate objects down to instances level. These requirements produce some unique challenges especially for deep learning based GOT algorithms that may easily become over-fitting if given a really small training dataset of the object during the online tracking process. To deal with this issue, we propose a novel Reptile meta-tracking algorithm, which adopts a first-order meta-learning technique so that during initialization, the visual tracker only requires few training examples and few steps of optimization to perform well. The proposed Reptile meta-tracker is evaluated on OTB2015 and VOT2018 tracking benchmark datasets, and outperforms several state-of-the-art trackers using one-pass evaluation.
    Relation: IEEE
    DOI: 10.1109/AVSS.2019.8909863
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Proceeding

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