Deep real-time hand detectoin using CFPN on embedded systems

Pirdiansyah Hendri, Jun Wei Hsieh, Ping Yang Chen, M. Gochoo, Yong Sheng Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Real-time HI (Human Interface) systems need accurate and efficient hand detection models to meet the limited resources in budget, dimension, memory, computing, and electric power. In recent years, object detection became a less challenging task with the latest deep CNN-based state-of-the-art models, i.e., RCNN, SSD, and YOLO; however, these models cannot provide the desired efficiency and accuracy for HI systems on embedded devices due to their complex time-consuming architecture. In addition, the detection of small hands (<30x30 pixels) is still a challenging task for all the above existing methods. Thus, we propose a shallow model named Concatenated Feature Pyramid Network (CFPN) to provide above mentioned performance for small hand detection. The superiority of CFPN is confirmed on a HandFlow dataset with mAP:0.5 of 95.6 and FPS of 33 on Nvidia TX2. The COCO dataset is also used to compare with other state-of-the-art method and shows the highest efficiency and accuracy with the proposed CFPN model. Thus we conclude that the proposed model is useful for real-life small hand detection on embedded devices.

Original languageEnglish
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728188089
Publication statusPublished - 2020
Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: Jan 10 2021Jan 15 2021

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference25th International Conference on Pattern Recognition, ICPR 2020
CityVirtual, Milan


  • Air-writing
  • Edge computing
  • Embedded system
  • Hand detection
  • Human interface

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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