An Open-Space Museum as a Testbed forPopularity Monitoring in Real-World Settings
Marco CattaniDelft University of Technology
Ioannis ProtonotariosDelft University of Technology
Claudio MartellaVU University Amsterdam
Joost van VelzenSalland Electronics
Marco ZunigaDelft University of Technology
Koen LangendoenDelft University of Technology
AbstractThis paper reports our experience with crowd monitor-
ing technologies in the challenging real-world conditions ofa modern, open-space museum. We seized the opportunityto use the NEMO science center as a testbed, and studiedthe effectiveness of neighborhood discovery and density es-timation algorithms in a network formed by visitors wearingbracelets emitting RF beacons. The diverse set of conditions(flash crowds in open spaces vs. single person booths) re-vealed three interesting findings: (i) state-of-the-art densityestimation fails in 80% of the cases, (ii) RSS-based classi-fiers fail too, because their underlying assumptions do nothold in many scenarios, and (iii) neighborhood discoverycan obtain exact information in an energy-efficient way, pro-vided that static and mobile nodes are differentiated to filterout passers by clobbering the true popularity of an exhibit.The overall lesson from the experiment is that todays algo-rithms are quite far from the ideal of monitoring popularityin a privacy-preserving and energy-efficient way with mini-mal infrastructure across the set of heterogeneous conditionsencountered in practice.
Categories and Subject DescriptorsC.2.4 [Computer-Communication Networks]: Dis-
tributed SystemsDistributed Applications
General TermsMeasurement, Human Factors, Experimentation
Keywords Crowd monitoring, Density estimation1 Introduction
In this paper we report our experience with a system de-signed to monitor the popularity of different exhibits in alarge science museum. Due to the size and complexity of
the museum, the curators had a difficult time quantifying theinterest of thousands of daily visitors on the many activitiesand exhibits offered by the museum.The museum. The NEMO science center is the 5th mostvisited museum in The Netherlands, with half a million vis-itors per year. What makes this museum interesting as acase-study is not only its numbers, but rather three char-acteristics that are seldomly found in indoor environments.First, unlike other buildings that consist of clearly differen-tiated areas (rooms), the layout of NEMO can be seen as anopen 3D space deployed over six stories with few bound-aries (walls), cf. Figure 1. With more than 5,000 m2 of ex-hibition space, the lack of boundaries makes it difficult toestimate the number of people participating, or interested,in each particular exhibit. Second, the challenges posed bythe open-space layout are aggravated by the large numberof visitors and their random movement patterns. NEMOis targeted mainly for kids and it does not have suggestedroutes. With more than 3,000 visitors per day and an aver-age visit duration of three hours, at peak hours the museumcan host more than 2,000 visitors, all roaming around withno predetermined path. Third, NEMO has different types ofexhibitions. Some attract tens of visitors, others hundreds.Most of them are open experimentation areas where visitorsspend variable amounts of time. Other events are scheduledat particular times of the day, have a rather constant duration,and can take place in either open or closed areas.The monitoring system. The monitoring system deployedat NEMO was based on radio-frequency beacons and con-sisted of three main components: bracelets given to visi-tors, a network of anchor points placed at various points inthe museum, and a network of sniffers to collect the data.Bracelets were constantly exchanging discovery beaconswith similar hardware installed at each anchor point. Upon asuccessful encounter between a bracelet and an anchor node,a special packet describing the encounter was sent by thebracelet to the sniffer infrastructure, which committed thereceived information to a central database via the existingnetwork infrastructure (Wi-Fi and Ethernet). In terms of pri-vacy, the only requirement was for the data to be anonymous.Even though bracelets have IDs, we refrained from register-ing visitor details to avoid identification. For implementationdetails, we refer to .
Studio theater Workshop 1
Figure 1. Floor plan of the NEMO science center.
Considering the needs and requirements of the museumscurators and the monitoring system at hand, we proposedthe use of energy-efficient neighbor discovery mechanisms.Having each anchor point periodically monitor the surround-ing bracelets IDs provides sufficient information to track thepopularity of an exhibit.
Being aware, however, of the growing concerns usershave with ID tracking, the museum setup gave us the uniqueopportunity to also assess the performance of ID-less meth-ods in a highly-unstructured scenario. Our initial hypothesiswas simple: the community has developed methods to esti-mate node density without the need of IDs, and given thatthe popularity of an exhibit is determined by the number ofpeople in its surroundings, density estimation should be anaccurate proxy for popularity.Our contribution. The evaluation shows that density es-timators are ill-equipped to monitor popularity in scenarioswith open spaces, high dynamics, and heterogeneous typesof events. Our key findings are threefold. First, density esti-mators are not accurate in many cases with about 80 % of an-chor points obtaining an estimation error greater than 20 %.Second, density classifiers do not work in most cases, sincetheir underlying assumptions - lower mean and higher vari-ance for crowded places - only apply to few application sce-narios. Third, even if density estimators would be accurate,they cannot monitor popularity in scenarios having a mix-ture of mobile and static visitors. In these scenarios ID-lessmethods cannot differentiate the interested crowd (popular-ity) from the passersby. More work must be done to integratethe effect of crowd dynamics on ID-less methods.
2 Related WorkThe analysis of human mobility is of significant impor-
tance to gain fundamental insights about peoples activi-ties, needs, and interests. Thus, many studies focus on theanalysis of visitor interactions, flows, and the popularity ofpoints of interest within buildings [8, 12, 18], fairs , fes-tivals , and conferences . For the case of monitor-ing and analyzing the behavior of visitors in a museum, the
visualization of metrics such as popularity, attraction, hold-ing power, and flows has been explored to support the workof museum staff [6, 13]. Many studies have also utilizedsensors to measure the behavioral patterns of museum vis-itors. Earlier works focus on localizing visitors at coarse-grained levels (e.g., room level) through technologies likeBluetooth  to support multimedia guides [2, 16]. Theseworks are conducted on traditional compartmentalized mu-seums, focusing on classifying the visitors experience. Ourdeployment provides new insights based on the challengingscenario of an open floor plan and dense crowds.
2.1 Evaluated MethodsGiven the monitoring system available at the museum, we
looked for methods that are amenable to RF beacons. Weconsidered three methods: neighbor discovery, cardinalityestimators, and density classifiers.Neighbor Discovery. Neighbor discovery methods peri-odically monitor the set of devices in their radio range.For crowd-monitoring applications, neighbor discovery hasbeen proved useful to provide so-called crowd textures ab-stractions to detect different crowd dynamics, like pedes-trian lanes, congestions, and social groups , as well as touniquely associate mobile devices with static anchor pointsand reconstruct the complete sequence of locations visitedby a person .Cardinality Estimators. Compared to neighbor discovery,cardinality estimators trade information richness for speedand efficiency. To perform their estimations, these mecha-nisms often model the lower communication layers, search-ing for features that correlate to device density. The underly-ing principle is based on order statistics and is simple to fol-low: the more neighbors beaconing, the shorter the perceivedinter-beacon interval and the higher the estimated density.For example, NetDetect exploits the underlying distributionof packet transmissions , while Qian et. al. exploit thepackets arrival patterns in RFID systems for a fast estima-tion of tag cardinality . For our deployment, we imple-ment Estreme , which estimates neighborhood cardinal-ity by measuring the average time between periodic, asyn-chronous beacons. We chose Estreme because of its simplic-ity, high reported accuracy, and minimal parameter tuning.Density Classifiers. Density classifiers try to infer the den-sity of a crowd by exploiting correlations with signal strengthstatistics [10, 14, 15, 19, 20]. These techniques require plac-ing a set of wireless devices within the crowd, analyzingthe changes of several radio features as the crowd densitychanges, and training a classifier. A common premise is toassume that as the density of people increases, the mean RSSdecreases (due to bodies blocking radio communication) andthe RSS variance increases (due to the added multipath ef-fects). In our work we do not implement these classifiers,but focus on assessing if the RSS trends reported in the liter-ature hold in all scenari