Joint Sparsity-Based Robust Visual Tracking

Seyed Bozorgtabar, Roland GOECKE

Research output: A Conference proceeding or a Chapter in BookConference contribution

2 Downloads (Pure)

Abstract

In this paper, a new object tracking in a particle filter framework utilising a joint sparsity-based model is proposed. Based on the observation that a target can be reconstructed from several templates that are updated dynamically, we jointly analyse the representation of the particles under a single regression framework and with the shared underlying structure. Two convex regularisations are combined and used in our model to enable sparsity as well as facilitate coupling information between particles. Unlike the previous methods that consider a model commonality between particles or regard them as independent tasks, we simultaneously take into account a structure inducing norm and an outlier detecting norm. Such a formulation is shown to be more flexible in terms of handling various types of challenges including occlusion and cluttered background. To derive the optimal solution efficiently, we propose to use a Preconditioned Conjugate Gradient method, which is computationally affordable for high-dimensional data. Furthermore, an online updating procedure scheme is included in the dictionary learning, which makes the proposed tracker less vulnerable to outliers. Experiments on challenging video sequences demonstrate the robustness of the proposed approach to handling occlusion, pose and illumination variation and outperform state-of-the-art trackers in tracking accuracy.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing (ICIP)
Editors Pesquet-Popescu, Fowler
Place of PublicationParis, France
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4927-4931
Number of pages5
ISBN (Electronic)9781479957514
ISBN (Print)9781479957514
DOIs
Publication statusPublished - 2014
EventIEEE International Conference on Image Processing 2014 - Paris, Paris, France
Duration: 27 Oct 201430 Oct 2014

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014

Conference

ConferenceIEEE International Conference on Image Processing 2014
CountryFrance
CityParis
Period27/10/1430/10/14

    Fingerprint

Cite this

Bozorgtabar, S., & GOECKE, R. (2014). Joint Sparsity-Based Robust Visual Tracking. In Pesquet-Popescu, & Fowler (Eds.), 2014 IEEE International Conference on Image Processing (ICIP) (pp. 4927-4931). [7025998] (2014 IEEE International Conference on Image Processing, ICIP 2014). Paris, France: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICIP.2014.7025998