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Blob Detection in Static Camera with Gaussian Mixture and Silhouette Index for Human Counting Application
Wibowo F.Y.E.a, Hutabarat M.T.a, Sh M.S.a
a Faculty of Electrical Engineering, Institut Teknologi Bandung, Bandung, Jawa Barat, 40135, 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.The problem of the object counting in static camera, especially for human counting application, is blob detection. Because humans frequently interact each other, the background subtraction is not sufficient to detect the number of object. Clustering method using Gaussian Mixture Model and silhouette index to detect the number of person in static camera feed are proposed. The whole detection consists of 3 stages which are preprocess, foreground area sampling, and blob detection by clustering algorithm. The overall systems are evaluated using recoded video that was captured by camera with 30° inclination to horizontal plane. The evaluation result shows that the developed algorithm with Gaussian Mixture Model and silhouette index can distinguish 2 persons that walking side-by-side up to 95.35% detection rate and less effective to detect 2 person walking in a queue with maximum detection rate of 56.8%.[/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]Background subtraction,Blob detection,Clustering methods,Evaluation results,Gaussian Mixture Model,Maximum detection rates,Silhouette indices,Static cameras[/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]blob detection,Gaussian mixture model,silhouette index,Static camera[/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][/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/ISESD.2018.8605459[/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]