Multi-target tracking with sparse group features and position using discrete-continuous optimization

Billy Peralta, Alvaro Soto

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

Abstract

Multi-target tracking of pedestrians is a challenging task due to uncertainty about targets, caused mainly by similarity between pedestrians, occlusion over a relatively long time and a cluttered background. A usual scheme for tackling multi-target tracking is to divide it into two sub-problems: data association and trajectory estimation. A reasonable approach is based on joint optimization of a discrete model for data association and a continuous model for trajectory estimation in a Markov Random Field framework. Nonetheless, usual solutions of the data association problem are based only on location information, while the visual information in the images is ignored. Visual features can be useful for associating detections with true targets more reliably, because the targets usually have discriminative features. In this work, we propose a combination of position and visual feature information in a discrete data association model. Moreover, we propose the use of group Lasso regularization in order to improve the identification of particular pedestrians, given that the discriminative regions are associated with particular visual blocks in the image. We find promising results for our approach in terms of precision and robustness when compared with a state-of-the-art method in standard datasets for multi-target pedestrian tracking.

Original languageEnglish
Title of host publicationComputer Vision - 12th Asian Conference on Computer Vision, ACCV 2014, Revised Selected Papers
EditorsC.V. Jawahar, Shiguang Shan
PublisherSpringer Verlag
Pages680-694
Number of pages15
ISBN (Print)9783319166339
DOIs
Publication statusPublished - 1 Jan 2015
Event12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
Duration: 1 Nov 20142 Nov 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9010
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th Asian Conference on Computer Vision, ACCV 2014
CountrySingapore
CitySingapore
Period1/11/142/11/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Peralta, B., & Soto, A. (2015). Multi-target tracking with sparse group features and position using discrete-continuous optimization. In C. V. Jawahar, & S. Shan (Eds.), Computer Vision - 12th Asian Conference on Computer Vision, ACCV 2014, Revised Selected Papers (pp. 680-694). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9010). Springer Verlag. https://doi.org/10.1007/978-3-319-16634-6_49