A real-time person tracking system based on SiamMask network for intelligent video surveillance

Imran Ahmed, Gwanggil Jeon

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

Real-time video surveillance systems are widely deployed in various environments, including public areas, commercial buildings, and public infrastructures. Person detection is a key and crucial task in different video surveillance applications, such as person detection, segmentation, and tracking. Researchers presented different image processing and artificial intelligence-based approaches (including machine and deep learning) for person detection and tracking, but mainly comprised of frontal view camera perspective. A real-time person tracking and segmentation system is introduced in this work, using an overhead camera perspective. The system applied a deep learning-based algorithm, i.e., SiamMask, a simple, versatile, fast, and surpassing other real-time tracking algorithms. The algorithm also performs segmentation of the target person by combining a mask branch to the fully convolutional twin neural network for target or person tracking. First, the person video sequences are obtained from an overhead perspective, and then additional training is performed with the help of transfer learning. Finally, a comparison is performed with other tracking algorithms. The SiamMask algorithm delivers good results, with a tracking accuracy of 95%.

Original languageEnglish
Pages (from-to)1803-1814
Number of pages12
JournalJournal of Real-Time Image Processing
Volume18
Issue number5
DOIs
Publication statusPublished - Oct 2021
Externally publishedYes

Keywords

  • Deep learning
  • Image processing
  • Overhead view
  • Person tracking
  • SaimMask
  • Smart video surveillance

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