Combination of ACF detector and multi-task CNN for hand detection

Yizhang Xia*, Shiyang Yan, Bailing Zhang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

5 Citations (Scopus)


Hand detection is an important issue in the analysis of drivers activities, assessment of drivers alertness, and subsequent development of driver safety monitoring system. In this work, the hand detection problem is addressed in the deep Convolutional Neural Network (CNN) framework. Hypothesis of hand regions are first generated with high recall rate by AdaBoost detector associated with Aggregated Channel Features (ACF) and then the Convolutional neural networks (CNN) are employed to extract the features of each proposal regions. The CNN was trained via multi-task learning paradigm to detect hand and predict the corresponding bounding box simultaneously. Experiments were conducted on the publically available benchmark VIVA hand detection database, showing marked improvement upon previous works.

Original languageEnglish
Title of host publicationICSP 2016 - 2016 IEEE 13th International Conference on Signal Processing, Proceedings
EditorsYuan Baozong, Ruan Qiuqi, Zhao Yao, An Gaoyun
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509013449
Publication statusPublished - 2 Jul 2016
Event13th IEEE International Conference on Signal Processing, ICSP 2016 - Chengdu, China
Duration: 6 Nov 201610 Nov 2016

Publication series

NameInternational Conference on Signal Processing Proceedings, ICSP


Conference13th IEEE International Conference on Signal Processing, ICSP 2016


  • Aggregated Channel Features (ACF)
  • Convolutional Neural Network (CNN)
  • Hand detection
  • Multi-task learning


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