Real-time face detection and motorized tracking using ScicosLab and SMCube on SoC's

Wing Jack Lee; Kok Yew Ng; Chin Luh Tan; Chee Pin Tan

doi:10.1109/ICARCV.2016.7838614

Real-time face detection and motorized tracking using ScicosLab and SMCube on SoC's

Wing Jack Lee, Kok Yew Ng, Chin Luh Tan, Chee Pin Tan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

210 Downloads (Pure)

Abstract

This paper presents a method for real-time detection and tracking of the human face. This is achieved using the Raspberry Pi microcomputer and the Easylab microcontroller as the main hardware with a camera mounted on servomotors for continuous image feed-in. Real-time face detection is performed using Haar-feature classifiers and ScicosLab in the Raspberry Pi. Then, the Easylab is responsible for face tracking, keeping the face in the middle of the frame through a pair of servomotors that control the horizontal and vertical movements of the camera. The servomotors are in turn controlled based on the state-diagrams designed using SMCube in the EasyLab. The methodology is verified via practical experimentation.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	978-1-5090-3549-6
ISBN (Print)	978-1-5090-3549-6, 978-1-5090-4757-4
DOIs	https://doi.org/10.1109/ICARCV.2016.7838614
Publication status	Published (in print/issue) - 2 Feb 2017
Event	2016 14th International Conference on Control, Automation, Robotics & Vision Phuket, Thailand - Phuket, Thailand Duration: 2 Aug 2016 → …

Conference

Conference	2016 14th International Conference on Control, Automation, Robotics & Vision Phuket, Thailand
Period	2/08/16 → …

Keywords

real-time
face detection
face tracking
scicoslab
smcube
system-on-chip

Access to Document

10.1109/ICARCV.2016.7838614

ManuscriptAuthor Accepted Manuscript -, 4.07 MB

http://uir.ulster.ac.uk/38285/3/AS4599083290214401486662098158_content_1.pdf

Cite this

@inproceedings{aecd3148890d417684e3c61390884c0e,

title = "Real-time face detection and motorized tracking using ScicosLab and SMCube on SoC's",

abstract = "This paper presents a method for real-time detection and tracking of the human face. This is achieved using the Raspberry Pi microcomputer and the Easylab microcontroller as the main hardware with a camera mounted on servomotors for continuous image feed-in. Real-time face detection is performed using Haar-feature classifiers and ScicosLab in the Raspberry Pi. Then, the Easylab is responsible for face tracking, keeping the face in the middle of the frame through a pair of servomotors that control the horizontal and vertical movements of the camera. The servomotors are in turn controlled based on the state-diagrams designed using SMCube in the EasyLab. The methodology is verified via practical experimentation.",

keywords = "real-time, face detection, face tracking, scicoslab, smcube, system-on-chip",

author = "Lee, \{Wing Jack\} and Ng, \{Kok Yew\} and Tan, \{Chin Luh\} and Tan, \{Chee Pin\}",

year = "2017",

month = feb,

day = "2",

doi = "10.1109/ICARCV.2016.7838614",

language = "English",

isbn = "978-1-5090-3549-6",

pages = "1--6",

booktitle = "Unknown Host Publication",

publisher = "IEEE",

address = "United States",

note = "2016 14th International Conference on Control, Automation, Robotics \& Vision Phuket, Thailand ; Conference date: 02-08-2016",

}

TY - GEN

T1 - Real-time face detection and motorized tracking using ScicosLab and SMCube on SoC's

AU - Lee, Wing Jack

AU - Ng, Kok Yew

AU - Tan, Chin Luh

AU - Tan, Chee Pin

PY - 2017/2/2

Y1 - 2017/2/2

N2 - This paper presents a method for real-time detection and tracking of the human face. This is achieved using the Raspberry Pi microcomputer and the Easylab microcontroller as the main hardware with a camera mounted on servomotors for continuous image feed-in. Real-time face detection is performed using Haar-feature classifiers and ScicosLab in the Raspberry Pi. Then, the Easylab is responsible for face tracking, keeping the face in the middle of the frame through a pair of servomotors that control the horizontal and vertical movements of the camera. The servomotors are in turn controlled based on the state-diagrams designed using SMCube in the EasyLab. The methodology is verified via practical experimentation.

AB - This paper presents a method for real-time detection and tracking of the human face. This is achieved using the Raspberry Pi microcomputer and the Easylab microcontroller as the main hardware with a camera mounted on servomotors for continuous image feed-in. Real-time face detection is performed using Haar-feature classifiers and ScicosLab in the Raspberry Pi. Then, the Easylab is responsible for face tracking, keeping the face in the middle of the frame through a pair of servomotors that control the horizontal and vertical movements of the camera. The servomotors are in turn controlled based on the state-diagrams designed using SMCube in the EasyLab. The methodology is verified via practical experimentation.

KW - real-time

KW - face detection

KW - face tracking

KW - scicoslab

KW - smcube

KW - system-on-chip

UR - https://pure.ulster.ac.uk/en/publications/real-time-face-detection-and-motorized-tracking-using-scicoslab-a

UR - https://www.scopus.com/pages/publications/85015206136

U2 - 10.1109/ICARCV.2016.7838614

DO - 10.1109/ICARCV.2016.7838614

M3 - Conference contribution

SN - 978-1-5090-3549-6

SN - 978-1-5090-4757-4

SP - 1

EP - 6

BT - Unknown Host Publication

PB - IEEE

T2 - 2016 14th International Conference on Control, Automation, Robotics & Vision Phuket, Thailand

Y2 - 2 August 2016

ER -

Real-time face detection and motorized tracking using ScicosLab and SMCube on SoC's

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Mark Ng

Cite this

Real-time face detection and motorized tracking using ScicosLab and SMCube on SoC's

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Profiles

Mark Ng

Cite this