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Object detection and tracking using SIFT-KNN classifier and Yaw-Pitch servo motor control on humanoid robot

Putri D.I.H.a, Martina, Riyantoa, Machbub C.a

a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Jawa Barat, 40132, Indonesia

[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 IEEE.Computer vision is a technology intended to replace the visual function in humans with extracting information and features from an image and analyzing the information. In this paper discussed the process of design and implementation of object tracking system that was built starting with object recognition in the early stages and then equipped with yaw and pitch system for tracking the position of the object on bioloid GP. SIFT algorithm is used as a feature extraction, KNN is used as a classifier and for estimation of homographic changes in objects using RANSAC. In order for objects to be tracked automatically, PID control is used to correct the coordinates obtained when object recognition with center coordinates of the frame. So, the system can track the object with single or dynamic displacement.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]BIOLOID,Design and implementations,Dynamic displacements,Extracting information,Object detection and tracking,RANSAC,SIFT,Yaw-Pitch Movement[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bioloid GP,KNN,PID,RANSAC,SIFT,Yaw-Pitch Movement[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]ACKNOWLEDGMENT This work was supported by Program of Post Graduate Team Research 2017 from The Ministry of Research, Technology and Higher Education, Republic of Indonesia[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICSIGSYS.2018.8373566[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]