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

Billy Peralta, Alvaro Soto

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferenciarevisión exhaustiva


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.

Idioma originalInglés
Título de la publicación alojadaComputer Vision - 12th Asian Conference on Computer Vision, ACCV 2014, Revised Selected Papers
EditoresC.V. Jawahar, Shiguang Shan
EditorialSpringer Verlag
Número de páginas15
ISBN (versión impresa)9783319166339
EstadoPublicada - 1 ene. 2015
Evento12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapur
Duración: 1 nov. 20142 nov. 2014

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


Conferencia12th Asian Conference on Computer Vision, ACCV 2014

Áreas temáticas de ASJC Scopus

  • Ciencia computacional teórica
  • Informática (todo)


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