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Enhancement of Missing Face Prediction Algorithm with Kalman Filter and DCF-CSR
Maharani D.A.a, MacHbub C.a, Rusmin P.H.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, 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]© 2019 IEEE.Detection and tracking of moving objects in sequence videos has wide applications in security surveillance, and becomes concern to many researchers. In actual environmental conditions, with various lighting conditions, object tracking faces a number of challenges including partial or severe occlusion which causes some systems to lose information so that it is difficult to estimate object trajectory. In the domain of surveillance, human tracking should not only be based on face, but also based on other characteristics, so that wherever the person facing towards, the system is always able to do the tracking correctly. In this study the detection and alignment process employed Multi-task Cascaded Convolutional Networks and Kalman Filters to predict facial position. Then, at the times the face is not facing towards the camera, the system saves the color of the bounding box that was last seen and tracks by color using the Discriminative Correlation Filter with Channel and Spatial Reliability (DCF-CSR). The proposed method resulting in increasing a person’s detection rate when facing away from the 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=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Convolutional networks,Correlation filters,Detection and tracking of moving objects,Environmental conditions,Lighting conditions,Object trajectories,Prediction algorithms,Security surveillance[/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]Cascade Convolutional Neural Network,Discriminative Correlation Filter with Channel and Spatial Reliability,face tracking,Kalman Filter,moving prediction[/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 research is a part of Research, Community Services, and Inovation program (Program Penelitian, Pengabdian kepada Masyarakat dan Inovasi / P3MI) funded by Institute for Research and Community Services at the Institut Teknologi Bandung (LPPM ITB).[/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/ICEEI47359.2019.8988867[/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]