An Optimization of Video Sequence Stitching Method

Yu, Hong; Kong, Liuyang

doi:10.1109/icma.2018.8484309

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Toapanta et al [19] conducted a study of SIFT, SURF, and ORB methods to recognize human identity through iris that the highest result is ORB with accuracy of 99.6%. Yu and Kong [20] also compared Harris Corner, SURF, and ORB for stitching frames in videos that result in the fastest algorithm is ORB, 0.105 seconds per processing. Rublee et al [16] also study image processing by taking 1000 features.…”

Section: Introductionmentioning

confidence: 99%

Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm

Adhinata¹,

Harjoko²,

Wahyono³

2021

International Journal on Advanced Science, Engineering and Information Technology

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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.

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Section: Introductionmentioning

confidence: 99%