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    Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment

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    Name:
    1902.08231v1.pdf
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    3.891Mb
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    Genre
    Pre-print
    Date
    2019
    Author
    Chu, Peng
    Fan, Heng
    Tan, Chiu C
    Ling, Haibin
    IEEE
    Subject
    cs.CV
    cs.CV
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/4587
    
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    DOI
    10.1109/WACV.2019.00023
    Abstract
    Recent progresses in model-free single object tracking (SOT) algorithms have largely inspired applying SOT to \emph{multi-object tracking} (MOT) to improve the robustness as well as relieving dependency on external detector. However, SOT algorithms are generally designed for distinguishing a target from its environment, and hence meet problems when a target is spatially mixed with similar objects as observed frequently in MOT. To address this issue, in this paper we propose an instance-aware tracker to integrate SOT techniques for MOT by encoding awareness both within and between target models. In particular, we construct each target model by fusing information for distinguishing target both from background and other instances (tracking targets). To conserve uniqueness of all target models, our instance-aware tracker considers response maps from all target models and assigns spatial locations exclusively to optimize the overall accuracy. Another contribution we make is a dynamic model refreshing strategy learned by a convolutional neural network. This strategy helps to eliminate initialization noise as well as to adapt to the variation of target size and appearance. To show the effectiveness of the proposed approach, it is evaluated on the popular MOT15 and MOT16 challenge benchmarks. On both benchmarks, our approach achieves the best overall performances in comparison with published results.
    Citation to related work
    IEEE
    Has part
    2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV)
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    For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.34944/dspace/4569
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