Deep location-specific tracking

Lingxiao Yang, Risheng Liu, David Zhang, Lei Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

20 Citations (Scopus)

Abstract

Convolutional Neural Network (CNN) based methods have shown significant performance gains in the problem of visual tracking in recent years. Due to many uncertain changes of objects online, such as abrupt motion, background clutter and large deformation, the visual tracking is still a challenging task. We propose a novel algorithm, namely Deep Location-Specific Tracking, which decomposes the tracking problem into a localization task and a classification task, and trains an individual network for each task. The localization network exploits the information in the current frame and provides a specific location to improve the probability of successful tracking, while the classification network finds the target among many examples generated around the target location in the previous frame, as well as the one estimated from the localization network in the current frame. CNN based trackers often have massive number of trainable parameters, and are prone to over-fitting to some particular object states, leading to less precision or tracking drift. We address this problem by learning a classification network based on 1 × 1 convolution and global average pooling. Extensive experimental results on popular benchmark datasets show that the proposed tracker achieves competitive results without using additional tracking videos for fine-tuning. The code is available at https://github.com/ZjjConan/DLST.

Original languageEnglish
Title of host publicationMM 2017 - Proceedings of the 2017 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages1309-1317
Number of pages9
ISBN (Electronic)9781450349062
DOIs
Publication statusPublished - 23 Oct 2017
Event25th ACM International Conference on Multimedia, MM 2017 - Mountain View, United States
Duration: 23 Oct 201727 Oct 2017

Publication series

NameMM 2017 - Proceedings of the 2017 ACM Multimedia Conference

Conference

Conference25th ACM International Conference on Multimedia, MM 2017
Country/TerritoryUnited States
CityMountain View
Period23/10/1727/10/17

Keywords

  • Convolutional neural networks
  • Location specific tracking
  • Single object tracking
  • Visual tracking

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Software

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