Online multiple object tracking via flow and convolutional features

Lu Wang, Lisheng Xu, Min Young Kim, Luca Rigazico, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

We propose an online multiple object tracking algorithm that exploits optical flow and convolutional features to handle noisy detections as well as frequent occlusion. To achieve robust tracking, we develop a data association method that deals with tracking scenarios of increasing difficulty. For easy scenarios, we use motion affinity to associate detections with objects. For ambiguous situations, we propose to use an appearance model based on convolutional features and correlation filters to complement template matching methods. For difficult cases where objects are under heavy occlusion, we carry out occlusion analysis, which exploits the relationship between targets and occluders to predict potential object locations. To deal with noisy detections, false positives are detected and removed on both raw detection and tracklet levels, while missing and inaccurate detections are recovered or corrected via short-term tracking. Experimental results on two benchmark datasets demonstrate that the proposed online algorithm performs favorably against the state-of-the-art methods.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages3630-3634
Number of pages5
ISBN (Electronic)9781509021758
DOIs
Publication statusPublished - 2018 Feb 20
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 2017 Sep 172017 Sep 20

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Other

Other24th IEEE International Conference on Image Processing, ICIP 2017
Country/TerritoryChina
CityBeijing
Period17/9/1717/9/20

Bibliographical note

Funding Information:
In this paper, we propose an online multiple object tracking algorithm. The proposed data association scheme uses motion, appearance affinities and occlusion analysis to solve object-detection assignments for different cases of increasing difficulty. To ensure reliable association, a convolutional correlation filter is adopted to compliment the template matching module based on optical flow. The CCF is also used to analyze visual tracking results to handle missing and inaccurate detections, as well as for robust association in occlusion analysis. Experimental results on two challenging benchmark datasets show that the proposed online algorithm performs favorably against the state-of-the-art methods. Acknowledgement Lu Wang is supported in part by Chinese Scholarship Council, in part by NSFC #61202258, and in part by NSF of Liaoning, China #20170540312 .

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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