Learning system for standing human detection

Author

Ammar, Boudour ; Rokbani, Nizar ; Alimi, Adel M.

Author_Institution

REGIM (Res. Group on Intell. Machines), Univ. of Sfax, Sfax, Tunisia

Volume

4

fYear

2011

fDate

10-12 June 2011

Firstpage

300

Lastpage

304

Abstract

The human detection is a key functionality to reach Human Computer and Robot Interaction. The human tracking is also a rapidly evolving area in computer and robot vision; it aims to explore and to follow human motion. We present in this article an intelligent system to learn human detection. The descriptors used in our system make up the combination of HOG and SIFT that capture salient features of humans automatically. Additionally, this system is employed to follow humans that it detects. Experimental results have been extracted for a set of sequences with standing and moving people at different positions and with a variation of backgrounds.

Keywords

gradient methods; image motion analysis; learning (artificial intelligence); object detection; object tracking; transforms; AdaBoost learning algorithm; HOG; SIFT; histogram of oriented gradient; human computer interaction; human motion dtection; human robot interaction; learning system; scale-invariant feature transform; standing human detection; Cameras; Databases; Feature extraction; Histograms; Humans; Robots; Video sequences; AdaBoost learning; HOG; Human detection; Human walker; SIFT descriptors;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-8727-1

Type

conf

DOI

10.1109/CSAE.2011.5952855

Filename

5952855