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Centroid-Tracking-Aided Robust Object Detection for Hospital Objects
Kinasih F.M.T.R.a, MacHbub C.a, Yulianti L.a, Rohman A.S.a
a Institut Teknologi Bandung, School of Electrical Engineering and Informatics, Bandung, 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]© 2020 IEEE.COVID-19 outbreak has a big impact to people’s daily life in 2020, especially in healthcare sector. As COVID-19 viruses are highly contagious, it is important to take strict measures to ensure all patients got the needed care while taking healthcare workers safety into consideration. Robot-based care is being hurriedly developed recently, and one of the important abilities for such robot is to be able to distinguish object commonly found in hospital thus the robot can make the correct action towards the correct object. For this publication, an object detector is trained to detect the hospital bed, thus it can be an input to the care robot navigation system when it is going to approach patients. As hospital beds vary from one brand to another, and this research has limited time constraint and readily available hardware, the object detector confidence is still low. Thus, a centroid tracking method is implemented to aid the hospital object detection, ensuring the robot can detect the correct bed more robustly with considerable speed for embedded implementation.[/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]Embedded implementation,Healthcare sectors,Healthcare workers,Object detectors,Robot navigation system,Robust object detection,Time constraints,Tracking method[/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]movement distance score,object detection,object tracking,speed (in frame per second)[/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 The authors thanked the Centre of Research and Community Service of the Institut Teknologi Bandung (LPPM ITB) for funding this research through the Program Penelitian dan Pengabdian Masyarakat (Research and Community Service Program) scheme.[/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/ICIDM51048.2020.9339679[/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]