RT info:eu-repo/semantics/article
T1 Centroid based person detection using pixelwise prediction of the position
A1 Dolezel, Petr
A1 Skrabanek, Pavel
A1 Stursa, Dominik
A1 Baruque Zanón, Bruno
A1 Cogollos Adrián, Héctor
A1 Kryda, Pavel
K1 Person detection
K1 Fully convolutional networks
K1 Performance measure
K1 Edge computing
K1 Computer vision
K1 Informática
K1 Computer science
K1 Fotografía
K1 Photography
AB Implementations of person detection in tracking and counting systems tend towards processing of orthogonally captured images on edge computing devices. The ellipse-like shape of heads in orthogonally captured images inspired us to predict head centroids to determine positions of persons in images. We predict the centroids using a fully convolutional network (FCN). We combine the FCN with simple image processing operations to ensure fast inference of the detector. We experiment with the size of the FCN output to further decrease the inference time. We compare the proposed centroid-based detector with bounding box-based detectors on head detection task in terms of the inference time and the detection performance. We propose a performance measure which allows quantitative comparison of the two detection approaches. For the training and evaluation of the detectors, we form original datasets of 8000 annotated images, which are characterized by high variability in terms of lighting conditions, background, image quality, and elevation profile of scenes. We propose an approach which allows simultaneous annotation of the images for both bounding box-based and centroid-based detection. The centroid-based detector shows the best detection performance while keeping edge computing standards.
PB Elsevier
SN 1877-7503
YR 2022
FD 2022-09
LK http://hdl.handle.net/10259/7113
UL http://hdl.handle.net/10259/7113
LA eng
NO The work was supported from ERDF/ESF “Cooperation in Applied Research between the University of Pardubice and companies, in the Field of Positioning, Detection and Simulation Technology for Transport Systems (PosiTrans)” (No. CZ.02.1.01/0.0/0.0/17_049/0008394).
DS Repositorio Institucional de la Universidad de Burgos
RD 04-may-2024