Title :
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
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;
Conference_Titel :
Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-8727-1
DOI :
10.1109/CSAE.2011.5952855
