Object tracking via collaborative multi-task learning and appearance model updating

Xu Cheng

doi:10.1016/j.asoc.2015.03.002

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Object tracking via collaborative multi-task learning and appearance model updating

2015-06-01

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DOI：10.1016/j.asoc.2015.03.002

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Abstract

En 中文

In this paper, we propose a novel visual tracking algorithm using the collaboration of generative and discriminative trackers under the particle filter framework. Each particle denotes a single task, and we encode all the tasks simultaneously in a structured multi-task learning manner. Then, we implement generative and discriminative trackers, respectively. The discriminative tracker considers the overall information of object to represent the object appearance; while the generative tracker takes the local information of object into account for handling partial occlusions. Therefore, two models are complementary during the tracking. Furthermore, we design an effective dictionary updating mechanism. The dictionary is composed of fixed and variational parts. The variational parts are progressively updated using Metropolis-Hastings strategy. Experiments on different challenging video sequences demonstrate that the proposed tracker performs favorably against several state-of-the-art trackers. (C) 2015 Elsevier B.V. All rights reserved.

Keywords:

Visual tracking

Particle filter

Sparse representation

Appearance modeling

Multi-task learninga

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