BibTeX
@article{IJASEIT12043,
author = {Faisal Dharma Adhinata and Agus Harjoko and - Wahyono},
title = {Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm},
journal = {International Journal on Advanced Science, Engineering and Information Technology},
volume = {11},
number = {6},
year = {2021},
pages = {2518--2526},
keywords = {Object searching; real-time video; keyframe selection; mutual information entropy; ORB.},
abstract = {The pre-processing and feature extraction stages are the primary stages in object searching on video data. Processing video in all frames is inefficient. Frames that have the same information should only be once processed to the next stage. Then, the feature extraction algorithm that is often used to process video frames is SIFT and SURF. The SIFT algorithm is very accurate but slow. On the other hand, the SURF algorithm is fast but less accurate. Therefore, the requirement for keyframe selection and feature extraction methods is fast and accurate in object searching on real-time video. Video is pre-processed by extracting video into frames. Then, the mutual information entropy method is used for keyframe selection. Keyframes are extracted using the ORB algorithm. The multiple object detection in the video is done by clustering on features. The feature extraction results on each cluster are matched with the results of the feature from the query image. Matching results from keyframe on video with the query image is used to retrieve the video's frame information. The experiment shows that keyframe selection is beneficial in real-time video data processing because the keyframe selection speed is faster than feature extraction on each frame. Then, feature extraction using the ORB algorithm results 2 times faster speed results than SIFT and SURF algorithms with values not so different from SIFT algorithm. This study's results can be developed as a security warning system in public places, especially by security in providing evidence of criminal cases from videos.},
issn = {2088-5334},
publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12043},
doi = {10.18517/ijaseit.11.6.12043}
}
EndNote
%A Adhinata, Faisal Dharma
%A Harjoko, Agus
%A Wahyono, -
%D 2021
%T Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm
%B 2021
%9 Object searching; real-time video; keyframe selection; mutual information entropy; ORB.
%! Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm
%K Object searching; real-time video; keyframe selection; mutual information entropy; ORB.
%X The pre-processing and feature extraction stages are the primary stages in object searching on video data. Processing video in all frames is inefficient. Frames that have the same information should only be once processed to the next stage. Then, the feature extraction algorithm that is often used to process video frames is SIFT and SURF. The SIFT algorithm is very accurate but slow. On the other hand, the SURF algorithm is fast but less accurate. Therefore, the requirement for keyframe selection and feature extraction methods is fast and accurate in object searching on real-time video. Video is pre-processed by extracting video into frames. Then, the mutual information entropy method is used for keyframe selection. Keyframes are extracted using the ORB algorithm. The multiple object detection in the video is done by clustering on features. The feature extraction results on each cluster are matched with the results of the feature from the query image. Matching results from keyframe on video with the query image is used to retrieve the video's frame information. The experiment shows that keyframe selection is beneficial in real-time video data processing because the keyframe selection speed is faster than feature extraction on each frame. Then, feature extraction using the ORB algorithm results 2 times faster speed results than SIFT and SURF algorithms with values not so different from SIFT algorithm. This study's results can be developed as a security warning system in public places, especially by security in providing evidence of criminal cases from videos.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12043
%R doi:10.18517/ijaseit.11.6.12043
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 6
%@ 2088-5334
IEEE
Faisal Dharma Adhinata,Agus Harjoko and - Wahyono,"Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 6, pp. 2518-2526, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.6.12043.
RefMan/ProCite (RIS)
TY - JOUR
AU - Adhinata, Faisal Dharma
AU - Harjoko, Agus
AU - Wahyono, -
PY - 2021
TI - Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm
JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 6
Y2 - 2021
SP - 2518
EP - 2526
SN - 2088-5334
PB - INSIGHT - Indonesian Society for Knowledge and Human Development
KW - Object searching; real-time video; keyframe selection; mutual information entropy; ORB.
N2 - The pre-processing and feature extraction stages are the primary stages in object searching on video data. Processing video in all frames is inefficient. Frames that have the same information should only be once processed to the next stage. Then, the feature extraction algorithm that is often used to process video frames is SIFT and SURF. The SIFT algorithm is very accurate but slow. On the other hand, the SURF algorithm is fast but less accurate. Therefore, the requirement for keyframe selection and feature extraction methods is fast and accurate in object searching on real-time video. Video is pre-processed by extracting video into frames. Then, the mutual information entropy method is used for keyframe selection. Keyframes are extracted using the ORB algorithm. The multiple object detection in the video is done by clustering on features. The feature extraction results on each cluster are matched with the results of the feature from the query image. Matching results from keyframe on video with the query image is used to retrieve the video's frame information. The experiment shows that keyframe selection is beneficial in real-time video data processing because the keyframe selection speed is faster than feature extraction on each frame. Then, feature extraction using the ORB algorithm results 2 times faster speed results than SIFT and SURF algorithms with values not so different from SIFT algorithm. This study's results can be developed as a security warning system in public places, especially by security in providing evidence of criminal cases from videos.
UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12043
DO - 10.18517/ijaseit.11.6.12043
RefWorks
RT Journal Article
ID 12043
A1 Adhinata, Faisal Dharma
A1 Harjoko, Agus
A1 Wahyono, -
T1 Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 6
YR 2021
SP 2518
OP 2526
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Object searching; real-time video; keyframe selection; mutual information entropy; ORB.
AB The pre-processing and feature extraction stages are the primary stages in object searching on video data. Processing video in all frames is inefficient. Frames that have the same information should only be once processed to the next stage. Then, the feature extraction algorithm that is often used to process video frames is SIFT and SURF. The SIFT algorithm is very accurate but slow. On the other hand, the SURF algorithm is fast but less accurate. Therefore, the requirement for keyframe selection and feature extraction methods is fast and accurate in object searching on real-time video. Video is pre-processed by extracting video into frames. Then, the mutual information entropy method is used for keyframe selection. Keyframes are extracted using the ORB algorithm. The multiple object detection in the video is done by clustering on features. The feature extraction results on each cluster are matched with the results of the feature from the query image. Matching results from keyframe on video with the query image is used to retrieve the video's frame information. The experiment shows that keyframe selection is beneficial in real-time video data processing because the keyframe selection speed is faster than feature extraction on each frame. Then, feature extraction using the ORB algorithm results 2 times faster speed results than SIFT and SURF algorithms with values not so different from SIFT algorithm. This study's results can be developed as a security warning system in public places, especially by security in providing evidence of criminal cases from videos.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12043
DO - 10.18517/ijaseit.11.6.12043
