Deep learning assisted robust visual tracking with adaptive particle filtering

Affiliation of Author(s):民航学院

Journal:SIGNAL PROCESSING-IMAGE COMMUNICATION

Key Words:Visual tracking Deep learning Particle filter

Abstract:We propose a novel visual tracking algorithm based on the representations from a pre-trained Convolutional Neural Network (CNN). Our algorithm pre-trains a simplified CNN using a large set of videos with tracking ground truths to obtain a generic target representation. When tracking, Particle Filtering (PF) is combined to the fully-connected layer in the pre-trained CNN. Deep representations and hand-crafted features help to model tracking. To optimize the particles' distribution, the velocity and acceleration information aids to calculate dynamic model. Meanwhile, our algorithm updates the tracking model in a lazy manner to avoid shift and expensive computation. As compared to previous methods, our results demonstrate superior performances in existing tracking benchmarks. (C) 2017 Elsevier B.V. All rights reserved.

Note:卷: 60 页: 183-192

ISSN No.:0923-5965

Translation or Not:no

Date of Publication:2018-02-01

Date of Publication:2018-02-01