Learning a real-time generic tracker using convolutional neural networks

Linnan Zhu, Lingxiao Yang, David Zhang, Lei Zhang

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

2 Citations (Scopus)

Abstract

This paper presents a novel frame-pair based method for visual object tracking. Instead of adopting two-stream Convolutional Neural Networks (CNNs) to represent each frame, we stack frame pairs as the input, resulting in a single-stream CNN tracker with much fewer parameters. The proposed tracker can learn generic motion patterns of objects with much less annotated videos than previous methods. Besides, it is found that trackers trained using two successive frames tend to predict the centers of searching windows as the locations of tracked targets. To alleviate this problem, we propose a novel sampling strategy for off-line training. Specifically, we construct a pair by sampling two frames with a random offset. The offset controls the moving smoothness of objects. Experiments on the challenging VOT14 and OTB datasets show that the proposed tracker performs on par with recently developed generic trackers, but with much less memory. In addition, our tracker can run in a speed of over 100 (30) fps with a GPU (CPU), much faster than most deep neural network based trackers.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Multimedia and Expo, ICME 2017
PublisherIEEE Computer Society
Pages1219-1224
Number of pages6
ISBN (Electronic)9781509060672
DOIs
Publication statusPublished - 28 Aug 2017
Event2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, Hong Kong
Duration: 10 Jul 201714 Jul 2017

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2017 IEEE International Conference on Multimedia and Expo, ICME 2017
CountryHong Kong
CityHong Kong
Period10/07/1714/07/17

Keywords

  • Convolutional neural networks
  • Generic object tracker
  • Real-time tracking
  • Single-target tracking

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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