Moving Object Tracking based on Kernel and Random-coupled Neural Network

Yiran Chen, Haoran Liu, Mingzhe Liu, Yanhua Liu, Ruili Wang, Peng Li

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Moving object tracking on cost-effective hardware is a crucial need in numerous research and industrial applications. However, current deep learning-based tracking algorithms usually prioritize exceptional performance at the expense of increased computational load. Due to the unavailability of expensive GPUs for many tracking tasks, these popular trackers often fall short in providing robust tracking capabilities with affordable computational resources. This study introduces RCNNshift, a kernel-based tracker that relies on feature extraction from a random-coupled neural network. This visual cortex inspired neural model can extract image features without requiring cumbersome pre-training or deep neural connections. By utilizing an enhanced one-dimensional feature representation, RCNNshift demonstrates superior performance compared to other kernel-based object tracking methods, even those employing higher-dimensional feature spaces. Its improvement in precision and success plots of OPE, compared to the Meanshift and Camshift in the HSV and RGB color spaces, exceeds over 160% and 190% respectively. Comparative experiments have validated the robustness of RCNNshift, showcasing its superior performance over various kernel-based and particle filter trackers. Its combination of robustness and computational efficiency makes RCNNshift an ideal choice for mid to low-end object tracking tasks such as surveillance and underwater tracking. The source code is available at https://github.com/HaoranLiu507/RCNNshift.

Original languageEnglish
Title of host publicationProceedings of the 6th ACM International Conference on Multimedia in Asia, MMAsia 2024
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9798400712739
DOIs
Publication statusPublished - 28 Dec 2024
Externally publishedYes
Event6th ACM International Conference on Multimedia in Asia, MMAsia 2024 - Auckland, New Zealand
Duration: 3 Dec 20246 Dec 2024

Publication series

NameProceedings of the 6th ACM International Conference on Multimedia in Asia, MMAsia 2024

Conference

Conference6th ACM International Conference on Multimedia in Asia, MMAsia 2024
Country/TerritoryNew Zealand
CityAuckland
Period3/12/246/12/24

Keywords

  • Kernel-based tracking
  • Moving object tracking
  • Radom-coupled neural network
  • Spiking neural networks

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

Fingerprint

Dive into the research topics of 'Moving Object Tracking based on Kernel and Random-coupled Neural Network'. Together they form a unique fingerprint.

Cite this