Enhanced Laplacian Group Sparse Learning with Lifespan Outlier Rejection for Visual Tracking

Seyed Bozorgtabar, Roland GOECKE

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Recently, sparse based learning methods have attracted much attention in robust visual tracking due to their effectiveness and promising tracking results. By representing the target object sparsely, utilising only a few adaptive dictionary templates, in this paper, we introduce a new particle filter based tracking method, in which we aim to capture the underlying structure among the particle samples using the proposed similarity graph in a Laplacian group sparse framework, such that the tracking results can be improved. Furthermore, in our tracker, particles contribute with different probabilities in the tracking result with respect to their relative positions in a given frame in regard to the current target object location. In addition, since the new target object can be well modelled by the most recent tracking results, we prefer to utilise the particle samples that are highly associated to the preceding tracking results. We demonstrate that the proposed formulation can be efficiently solved using the Accelerated Proximal method with just a small number of iterations. The proposed approach has been extensively evaluated on 12 challenging video sequences. Experimental results compared to the state-of-the-art methods demonstrate the merits of the proposed tracker.
Original languageEnglish
Title of host publication12th Asian Conference on Computer Vision (ACCV 2014)
Subtitle of host publicationLecture Notes in Computer Science
EditorsDaniel Cremers, Hideo Saito, Ian Reid, Ming-Hsuan Yang
Place of PublicationSwitzerland
Number of pages15
ISBN (Electronic)9783319168135
ISBN (Print)9783319168135
Publication statusPublished - 2015
Event12th Asian Conference on Computer Vision - Singapore, Singapore, Singapore
Duration: 1 Nov 20145 Nov 2014

Publication series

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


Conference12th Asian Conference on Computer Vision


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