Title :
Harris-SIFT Descriptor for Video Event Detection Based on a Machine Learning Approach
Author :
Camara-Chavez, G. ; de Albuquerque Araujo, Arnaldo
Author_Institution :
Comput. Sci. Dept., Fed. Univ. of Ouro Preto, Ouro Preto, Brazil
Abstract :
Video data is becoming increasingly important in many commercial and scientific areas with the advent of applications such as digital broadcasting, video-conferencing and multimedia processing tools, and with the development of the hardware and communications infrastructure necessary to support visual applications. The objective of this work is to propose a method for event detection in a video stream. We combine Harris-SIFT descriptor with motion information in order to detect human actions in video. We tested our method in KTH database and compared it to space-time interest points (STIP) descriptor. The results obtained achieved similar results to the STIP method.
Keywords :
image motion analysis; learning (artificial intelligence); object detection; video streaming; Harris-SIFT descriptor; KTH database; STIP descriptor; human action detection; machine learning; motion information; space-time interest points descriptor; video data; video event detection; video stream; Digital multimedia broadcasting; Digital video broadcasting; Event detection; Hardware; Humans; Machine learning; Motion detection; Multimedia communication; Streaming media; Testing; Harris-SIFT; STIP; SVM; video event detection;
Conference_Titel :
Multimedia, 2009. ISM '09. 11th IEEE International Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-5231-6
Electronic_ISBN :
978-0-7695-3890-7
DOI :
10.1109/ISM.2009.116
