Structure-Aware Local Sparse Coding for Visual Tracking
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TL;DR: A structure-aware local sparse coding algorithm is proposed, which encodes a target candidate using templates with both global and local sparsity constraints, and an effective template update scheme is designed to alleviate the issues with tracking drifts.
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Abstract: Sparse coding has been applied to visual tracking and related vision problems with demonstrated success in recent years Existing tracking methods based on local sparse coding sample patches from a target candidate and sparsely encode these using a dictionary consisting of patches sampled from target template images The discriminative strength of existing methods based on local sparse coding is limited as spatial structure constraints among the template patches are not exploited To address this problem, we propose a structure-aware local sparse coding algorithm, which encodes a target candidate using templates with both global and local sparsity constraints For robust tracking, we show the local regions of a candidate region should be encoded only with the corresponding local regions of the target templates that are the most similar from the global view Thus, a more precise and discriminative sparse representation is obtained to account for appearance changes To alleviate the issues with tracking drifts, we design an effective template update scheme Extensive experiments on challenging image sequences demonstrate the effectiveness of the proposed algorithm against numerous state-of-the-art methods
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Citations
VITAL: VIsual Tracking via Adversarial Learning
Yibing Song,Chao Ma,Xiaohe Wu,Lijun Gong,Linchao Bao,Wangmeng Zuo,Chunhua Shen,Rynson W. H. Lau,Ming-Hsuan Yang +8 more
- 18 Jun 2018
TL;DR: Zhang et al. as mentioned in this paper used a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes, and the network identifies the mask that maintains the most robust features of the target objects over a long temporal span.
VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results
Longyin Wen,Pengfei Zhu,Dawei Du,Xiao Bian,Haibin Ling,Qinghua Hu,Chenfeng Liu,Hao Cheng,Xiaoyu Liu,Wenya Ma,Qinqin Nie,Haotian Wu,Lianjie Wang,Asanka G. Perera,Baochang Zhang,Byeongho Heo,Chunlei Liu,Dongdong Li,Emmanouil Michail,Hanlin Chen,Hao Liu,Haojie Li,Ioannis Kompatsiaris,Jian Cheng,Jiaqing Fan,Jie Zhang,Jin-Young Choi,Jing Li,Jinyu Yang,Jongwon Choi,Juanping Zhao,Jungong Han,Kaihua Zhang,Kaiwen Duan,Ke Song,Konstantinos Avgerinakis,Kyuewang Lee,Lu Ding,Martin Lauer,Panagiotis Giannakeris,Peizhen Zhang,Qiang Wang,Qianqian Xu,Qingming Huang,Qingshan Liu,Robert Laganiere,Ruixin Zhang,Sangdoo Yun,Shengyin Zhu,Sihang Wu,Stefanos Vrochidis,Wei Tian,Wei Zhang,Weidong Chen,Weiming Hu,Wenhao Wang,Wenhua Zhang,Wenrui Ding,Xiaohao He,Xiaotong Li,Xin Zhang,Xinbin Luo,Xixi Hu,Yang Meng,Yangliu Kuai,Yanyun Zhao,Yaxuan Li,Yifan Yang,Yifan Zhang,Yong Wang,Yuankai Qi,Zhipeng Deng,Zhiqun He +72 more
- 08 Sep 2018
TL;DR: The evaluation protocol of the VisDrone-SOT2018 challenge is presented and the results of a comparison of 22 trackers on the benchmark dataset are presented, which are publicly available on the challenge website.
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•Posted Content
VITAL: VIsual Tracking via Adversarial Learning
Yibing Song,Chao Ma,Xiaohe Wu,Lijun Gong,Linchao Bao,Wangmeng Zuo,Chunhua Shen,Rynson W. H. Lau,Ming-Hsuan Yang +8 more
TL;DR: Zhang et al. as discussed by the authors used a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes, and the network identifies the mask that maintains the most robust features of the target objects over a long temporal span.
50
Unified Graph-Based Multicue Feature Fusion for Robust Visual Tracking
TL;DR: A robust object tracking framework based on the unified graph fusion (UGF) of multicue to adapt to the object’s appearance and a novel kernel-based adaptation strategy using outlier detection and a transductive reliability metric are presented.
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Spectral Filter Tracking
TL;DR: This paper proposes a simple but efficient spectral filter tracking method from the viewpoint of a graph, where each candidate’s image region is modeled as a pixelwise grid graph, and achieves the state-of-the-art performance on OTB-2015 and VOT2016 under the same feature extraction strategy.
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Online Object Tracking: A Benchmark
Yi Wu,Jongwoo Lim,Ming-Hsuan Yang +2 more
- 23 Jun 2013
TL;DR: Large scale experiments are carried out with various evaluation criteria to identify effective approaches for robust tracking and provide potential future research directions in this field.
Object Tracking Benchmark
TL;DR: An extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria is carried out to identify effective approaches for robust tracking and provide potential future research directions in this field.