Abstract
One of the main stages in object searching on video is extracting object regions from video. Template matching is popular technique for performing a such task. However, the use of template matching has a limitation that requires a large object as a template. If the template size is too small, it would obtain few features. On the other hand, ORB descriptors are often used for representing the object with a good accuracy and fast processing time. Therefore, this research proposed to use machine learning method combining with ORB descriptor for object searching on video data. Processing video in all frames is inefficient. Thus, frames are selected into keyframes using mutual information entropy. The ORB descriptors are then extracted from selected frame in order to find candidate region of objects. To verify and classify the object regions, multiclass support vector machine was used to train ORB descriptor of regions. For evaluation, the use of ORB would be compared with other descriptor, such as SIFT and SURF for showing its superiority in both accuracy and processing time. In experiment, it is found that object searching with ORB descriptor performs faster processing time, which is 0.219 s, while SIFT 1.011 s and SURF 0.503 s. Meanwhile, it also achieves the best F1 value, which is 0.9 compared to SIFT 0.63 and SURF 0.65.
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Acknowledgment
This work was supported by the 2020 Final Assignment Recognition Program (RTA) of Universitas Gadjah Mada (No. 2488/UN1.P.III/DIT-LIT/PT/2020).
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Adhinata, F.D., Harjoko, A., Wahyono (2020). Object Searching on Video Using ORB Descriptor and Support Vector Machine. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_20
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DOI: https://doi.org/10.1007/978-3-030-63119-2_20
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