Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	ecaade2016_070
id	ecaade2016_070
authors	Takagi, Naoya and Takizawa, Atsushi
year	2016
title	Development of The Method for Estimating Traffic Volume of Pedestrians in An Underground Mall by Use of Watch Cameras
source	Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 2, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 463-472
doi	https://doi.org/10.52842/conf.ecaade.2016.2.463
wos	WOS:000402064400046
summary	This paper describes a method for estimating pedestrian traffic volume by using video cameras. In the Umeda underground mall in Osaka City, we estimated the traffic volume without tracking technology and while protecting pedestrian's privacy. We developed an original algorithm that roughly estimates the traffic volume of pedestrians from sequential images of video cameras. We focused on a line on each image cut out from video and made a new image which shows the spatiotemporal distribution of pedestrians. We defined this image as 'time historical image of pedestrian spots (THIPS)'. In a THIPS, a pedestrian is regarded as a cluster of connected pixels with the same label. We captured the spatiotemporal distribution of pedestrians by using these images. We found that this algorithm requires a THIPS to estimate the number of pedestrians who passed the spot for a few minutes and plural THIPSs to estimate their traveling directions. Finally, we concluded that this algorithm is an efficient means of estimating pedestrian traffic volume.
keywords	Pedestrian Flow; Underground Mall; Spatiotemporal Distribution; Watch Cameras; Background Subtraction; Integer Linear Problem
series	eCAADe
email
last changed	2022/06/07 07:56

_id	ecaade2016_120
id	ecaade2016_120
authors	Takizawa, Atsushi
year	2016
title	Estimating Potential Event Occurrence Areas in Small Space based on Semi-supervised Learning
source	Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 2, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 169-178
doi	https://doi.org/10.52842/conf.ecaade.2016.2.169
wos	WOS:000402064400016
summary	We propose a method for relatively small space that can optimize the size and shape of the neighborhood of an event occurrence spot on a grid space to minimize the classification error using classification by aggregating emerging patterns based on the concept of semi-supervised learning. We apply this method to data of waiting people in the Umeda Underground Mall and show that the proposed method can improve classification accuracy and understandability of classification rules.
keywords	Small space; spatial event; clustering; classification; mixed integer quadratic programing
series	eCAADe
email
last changed	2022/06/07 07:56

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