@article{ART001941601},
author={Kwangseok Lee and 김영섭},
title={Pedestrian Detection and Tracking for Intelligent Video Surveillance},
journal={Journal of Knowledge Information Technology and Systems},
issn={1975-7700},
year={2014},
volume={9},
number={6},
pages={699-705}
TY - JOUR
AU - Kwangseok Lee
AU - 김영섭
TI - Pedestrian Detection and Tracking for Intelligent Video Surveillance
JO - Journal of Knowledge Information Technology and Systems
PY - 2014
VL - 9
IS - 6
PB - Korea Knowledge Information Technology Society
SP - 699
EP - 705
SN - 1975-7700
AB - In the development of detection and trace system, the algorithms for it are generally classified into three methods. These are background image based method, temporal differencing based method, and probability based method. Of them all, difference based method is used the most because it is easy for implement and efficient, and it is divided into recursive one and non-recursive one. Gaussian complex model is suggested in typical recursive method, and eigen background in typical non-recursive one.This research propose the trace technology to the detected object after selecting pedestrian as the region of interest in the multi-object environment contained progressive input video of the video collection device of the digital camera or CCTV. After this method separate object from background using the excluded MAMF (Modified-AMF) to the processing of the positively curved region in the typical background technique, AMF (Approximated Median Filtering), and choose region of interest to the image contained various objects effectively as combining the characteristic image using magnitude of the region distribution contained spatial properties and statistical properties, and improved trace of the detected object effectively as applying CAMShift (Continuously Adaptive Meanshift) algorithm to the selected region of interest for the improvement of the calculation speed.
KW - Background separation;Selection of region of interest;BGS (Background Subtraction) algorithms;Object trace using CAMShift
DO -
UR -
ER -
Kwangseok Lee and 김영섭. (2014). Pedestrian Detection and Tracking for Intelligent Video Surveillance. Journal of Knowledge Information Technology and Systems, 9(6), 699-705.
Kwangseok Lee and 김영섭. 2014, "Pedestrian Detection and Tracking for Intelligent Video Surveillance", Journal of Knowledge Information Technology and Systems, vol.9, no.6 pp.699-705.
Kwangseok Lee, 김영섭 "Pedestrian Detection and Tracking for Intelligent Video Surveillance" Journal of Knowledge Information Technology and Systems 9.6 pp.699-705 (2014) : 699.
Kwangseok Lee, 김영섭. Pedestrian Detection and Tracking for Intelligent Video Surveillance. 2014; 9(6), 699-705.
Kwangseok Lee and 김영섭. "Pedestrian Detection and Tracking for Intelligent Video Surveillance" Journal of Knowledge Information Technology and Systems 9, no.6 (2014) : 699-705.
Kwangseok Lee; 김영섭. Pedestrian Detection and Tracking for Intelligent Video Surveillance. Journal of Knowledge Information Technology and Systems, 9(6), 699-705.
Kwangseok Lee; 김영섭. Pedestrian Detection and Tracking for Intelligent Video Surveillance. Journal of Knowledge Information Technology and Systems. 2014; 9(6) 699-705.
Kwangseok Lee, 김영섭. Pedestrian Detection and Tracking for Intelligent Video Surveillance. 2014; 9(6), 699-705.
Kwangseok Lee and 김영섭. "Pedestrian Detection and Tracking for Intelligent Video Surveillance" Journal of Knowledge Information Technology and Systems 9, no.6 (2014) : 699-705.
