Staffordshire University logo
STORE - Staffordshire Online Repository

SiaMemory: Target Tracking

SEDKY, Mohamed, Chang, Li Bo, Zhang, shang Bing, Du, Hui Min and Wang, Shi yu (2019) SiaMemory: Target Tracking. Procedia Computer Science, 154. pp. 146-153. ISSN 1877-0509

[img]
Preview
Text
SiaMemory-Target Tracking.pdf - Publisher's typeset copy
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

Download (1MB) | Preview

Abstract or description

This paper proposes, develops and evaluates a novel object-tracking algorithm that outperforms start-of-the-art method in terms of its robustness. The proposed method compromises Siamese networks, Recurrent Convolutional Neural Networks (RCNNs)
and Long Short Term Memory (LSTM) and performs short-term target tracking in real-time. As Siamese networks only generates the current frame tracking target based on the previous frame of image information, it is less effective in handling
target’s appearance and disappearance, rapid movement, or deformation. Hence, our method a novel tracking method that integrates improved full-convolutional Siamese networks based on all-CNN, RCNN and LSTM. In order to improve the training
efficiency of the deep learning network, a strategy of segmented training based on transfer learning is proposed. For some test video sequences that background clutters, deformation, motion blur, fast motion and out of view, our method achieves the best tracking performance. Using 41 videos from the Object Tracking Benchmark (OTB) dataset and considering the area under the curve for the precision and success rate, our method outperforms the second best by 18.5% and 14.9% respectively.

Item Type: Article
Uncontrolled Keywords: object tracking; deep learning, Siamese networks, recurrent convolutional neural network
Faculty: School of Digital, Technologies and Arts > Computer Science, AI and Robotics
Depositing User: Mohamed SEDKY
Date Deposited: 18 Mar 2022 11:38
Last Modified: 18 Mar 2022 11:38
URI: https://eprints.staffs.ac.uk/id/eprint/7235

Actions (login required)

View Item View Item

DisabledGo Staffordshire University is a recognised   Investor in People. Sustain Staffs
Legal | Freedom of Information | Site Map | Job Vacancies
Staffordshire University, College Road, Stoke-on-Trent, Staffordshire ST4 2DE t: +44 (0)1782 294000