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1 A Study of Pedestran Observaton System wth Ultrasonc Dstance Sensor Shohe MINOMI, Hrosh YAMAMOTO, Katsuch NAKAMURA, Katsuyuk YAMAZAKI Nagaoka Unversty of Technology, Japan Network Applcaton Engneerng Laboratores Ltd., Japan Abstract A measurement of the pedestran traffc becomes a key ssue for supportng gudance of safety and for nvestgatng a vewer s evaluaton of an advertsement n a shoppng dstrct. In ths paper, n order to measure the pedestran traffc, we propose a new pedestran observaton system usng ultrasonc dstance sensors. From an expermental evaluaton n a shoppng street, we have confrmed that the proposed system accurately detects a trend of the pedestran traffc. Moreover, we have concluded that the proposed system can estmate the congeston of the shoppng street from the number of pedestrans derved from sensor data. Furthermore, we have confrmed that the proposed system can estmate the attenton level of dgtal sgnage programs by usng sensor data. The attenton level can be utlzed for mprovng the contents of the dgtal sgnage. Keywords Sensor Network, Dgtal Sgnage, Ultrasonc Dstance Sensor, Pedestran Traffc Measurement I. INTRODUCTION A pedestran traffc measurement becomes a key ssue for supportng human actvtes n varous areas. For example, n shoppng streets, f a manager of the shoppng street can detect the congeston of the street by some means, t s useful for supportng gudance of safety, evacuaton, etc. On the other hand, a dgtal sgnage s spreadng as a new advertsng meda nstead of a paper advertsement, and an owner of an advertsement can easly change the contents of the dgtal sgnage by consderng the vewer s evaluaton of the advertsement. In these applcatons, the amount and the trend of the pedestran traffc may be utlzed as a measure of attenton to the sgnage program. Ths s because the pedestrans change ther movng drectons or speeds when lookng at the dgtal sgnage. In order to measure the pedestran traffc, a camera has been generally used. However, use of the camera has a problem n terms of prvacy or cost. These problems also exst n a feld of the shoppng street, for whch a camera s used to detect persons walkng around the street or vewng the sgnage. In order to solve these problems, we propose a new pedestran observaton system usng low-cost ultrasonc dstance sensors. Furthermore, the effectveness of the proposed system s presented through an experment n the shoppng dstrct. II. RELATED WORKS AND OBJECTIVES OF STUDY In a feld of a measurement of the pedestran traffc, the method of measurng pedestrans by usng a camera has been proposed[1]. Ths method captures persons and tracks the number of pedestrans by analyzng recorded mage data. However, ths method s not suted for measurement n the nghttme, etc. Moreover, ths method has some prvacy concerns and may be dffcult to be nstalled n some areas. On the other hand, n order to measure the number of pedestrans drectly, the method usng 3D laser scanner has also been proposed[2]. Ths method detects pedestrans by measurng a dstance based on round trp tme of a laser pulse. However, the 3D laser scanner s very expensve and large, so the nstallaton becomes dffcult. Consderng these stuatons, we propose to use an ultrasonc dstance sensor, whch makes t possble to construct small and prvacy free measurement envronment. We also propose the method to detect veloctes of pedestrans, by whch we can estmate an attenton level of dgtal sgnage contents. III. PROPOSED SYSTEM A. Overvew of the Proposed System Arkas Sensor Network Manager of Shoppng Street CM programs Check & Improvement Attenton Lst Alert for pedestrans Server 1day nterval Nose Flterng Velocty Calculaton Attenton Estmaton Attenton Sensor Data Fgure 1. An overvew of the proposed system. 1mn nterval Nose Flterng Person Detecton Congeston Estmaton Congeston Fgure 1 shows an overvew of the proposed pedestran observaton system. In ths system, the server estmates two parameters, whch are 1) the congeston of the street and 2) the attenton of the dgtal sgnage, by analyzng a sensor data. ISBN Feb. 19~22, 212 ICACT212

2 In ths study, the congeston expresses an extent of the number of pedestrans of area where the system s nstalled. The attenton expresses an extent that pedestrans are lookng at the sgnage. The output of ths proposed system has twofold; 1) estmaton of pedestran populaton and 2) estmaton of attenton of dgtal sgnage contents. Output 1), pedestran populaton, can be used for analyss of crowd n that area. It may be further utlzed for safety gudance n the shoppng street etc. For example, an alert may be ssued to pedestrans when a street becomes so congested or suddenly crowded. So, ths congeston estmaton should be done n real tme, e.g., one mnute nterval. Output 2), attenton estmaton can be used to evaluate contents of the dgtal sgnage, e.g., whch contents have less attenton from pedestrans than others. A manager may change those less-attenton contents to others so that an effect of the dgtal sgnage can be mproved. Therefore, the attenton estmaton can be processed daly. B. Sensors for Detectng Pedestrans and ther Walkng Velocty Fgure 2 shows an dea to detect pedestrans and ther walkng velocty smultaneously. The sensor network s constructed by ultrasonc dstance sensors and Arkas sensor nodes[3], and s nstalled near the dgtal sgnage. In the proposed system, the sensor measures a dstance to pedestrans. By analyzng a sensed data, the server detects a pedestran s movng drecton and velocty. From these veloctes, the proposed system estmates whether pedestrans are lookng at the dgtal sgnage or not, because pedestrans may reduce ther speed when lookng at the dgtal sgnage. As shown n Fgure 2(a), ths system targets the dgtal sgnage that s vewable from only one drecton. In the precedng study[4], the ultrasonc dstance sensor was nstalled n the wall so that the ultrasonc wave crosses the street. However, ths method has a problem that the system can detect a person from a sensor data but cannot detect a pedestran s movng drecton and a velocty. In order to solve ths problem, n the proposed method, the sensor s nstalled n the wall by nclnng the angle as shown n Fgure 2(a). Fgure 2(b) shows an example of a sensor data. As shown n ths fgure, pedestran s movng drectons can be dstngushed based on a dfference of a tlt. Furthermore, veloctes of pedestrans can be derved based on an extent of a tlt. For example, a tlt becomes a gentle slope when velocty of pedestran s slow, and becomes a sharp slope when velocty of pedestran s fast. The ultrasonc dstance sensor calculates a dstance to objects from round trp tme of the ultrasonc wave, and can detect a person wthn about 4 [cm]. Unlke the system usng a camera, the ultrasonc dstance sensor enables a measurement n the nghttme, and does not cause prvacy ssues. Dstande [cm] Sensor Dgtal Sgnage Sensor : Vewable drecton : Not vewable drecton Data Arkas Sensor Node (a) Inclned nstallaton of sensors Server 1 Not Vewable Drecton Vewable Drecton Elapsed tme [mn] (b) An example of sensor data Fgure 2. Methods of detectng movng drecton and velocty. C. Arkas Sensor Platform A sensor platform wll further facltate ths deployment by provdng bult-n data transfer between sensor nodes and a server. By connectng sensors to a sensor node, one can get data from a server. Fgure 3 shows an overvew of Arkas sensor platform. Two types of sensors are supported by Arkas at present, 1) ON/OFF or Open/Short data and 2) analog or voltage data. One does not need to manpulate or program the Arkas node. ON/OFF data of one bt and analog data of 1 bts samplng can be sent to the server wth specfed ntervals. The Arkas sensor node supports up to 7 analog nterfaces and 16 ON/OFF nterfaces. Furthermore, the sensor node s also qute small, 6.5cm wdth x 6.5cm length x 2.5cm heght, and s desgned for a few power consumng. It can be operated by a solar battery, but was powered by commercal electrcty n the shoppng street. Up to 16 Arkas sensor nodes can be connected to one server. The server s nothng but a software package bult over Java, and can be operated over any platform (Wndows, Lnux, etc.). In the proposed system, we use a Lnux server for evaluatng experments. In the proposed system, the Arkas sensor node receves a dstance data wth analog nterface from sensors wth 1 [msec] ntervals, and sends data to a server. The Arkas sensor node s server-clent type sensor platform, and there s no need to nstall analyzng programs on the node. Accordngly, we can extend the number of sensor nodes easly. ISBN Feb. 19~22, 212 ICACT212

3 Sensors ZgBee Arkas sensor nodes Fgure 3. Overvew of Arkas sensor platform. Server OS (Lnux) Java Arkas Software IV. DETAIL METHOD OF THE PROPOSED SYSTEM A. Method of Congeston Estmaton In the frst step of congeston estmaton, the server performs a nose flterng to elmnate the nose. Fgure 4 shows an example of the nose flterng. In ths algorthm, the followng data are consdered as the nose. Dstance data over 4 [cm]. Dstance changed largely for a few consecutve measurement nstances. Regardng the frst tem, the server modfes the data nto 4 [cm]. Regardng the second tem, we decded whether the data s a nose or not based on the average of data and the standard devaton. The server judges data satsfyng the followng condton as a nose, and elmnates that. d : Sensor data [cm] at tme (1 [msec] ntervals) n 1 d (1) j 2n 1 jn n 1 2 (2) d j 2n 1 jn f ( d or d ) then d s nose. In the above condton, n (e.g., 1 to 5) wll be decded takng account of nose flterng performance. Before flterng Dstance data [cm] Data over 4 cm In the fnal step, the server estmates the congeston based on the number of pedestrans for each one mnute. In ths algorthm, the congeston s dvded nto several levels based on a threshold of the number of pedestrans. Above mentoned processes are performed wth one mnute nterval, and the system may alert pedestrans based on the latest congeston. B. Method of Attenton Estmaton In the frst step of attenton estmaton, n order to elmnate the nose, the server performs a nose flterng n the same way as the method of congeston estmaton. After flterng the nose from a sensor data, the server also extracts a part of a tlt from the fltered data. Fgure 5 shows an example of the extracton for calculatng veloctes of pedestrans. In ths algorthm, n order to accurately calculate the velocty, the server extracts a hgh lnearty tlt based on correlaton coeffcent. The server performs a lnearzaton for an extracted tlt, and derves a velocty accordng to the followng equaton. C : A coeffcent of the lnear expresson LE v [ km / h] C 4.6 : Velocty of person (3) P LE In ths algorthm, we confrmed that the constant for calculatng the velocty was 4.6 from the result of experment that was performed to nvestgate the relatonshp between coeffcents of the lnear expresson and veloctes. A sgn of C (plus or mnus) ndcates the pedestran s movng LE drecton. In the fnal step, the server estmates an attenton of ndvdual dgtal sgnage program based on veloctes of pedestrans. In ths system, t s assumed that the dgtal sgnage program.e., tme table of sgnage contents, s avalable before estmatng an attenton of the sgnage. Moreover, n ths algorthm, the attenton s dvded nto several levels based on a threshold of a velocty. Dstance data [cm] 4 Fltered data Extracted part After flterng Changed largely C LE Elapsed tme [mn] Fgure 4. An example of nose flterng. In the next step, n order to detect persons, the server extracts a part of a tlt from the fltered data as shown n Fgure 2(b). Addtonally, the server consders an extracted tlt to be one person Elapsed tme [mn] Fgure 5. An example of extracton. After analyzng sensor data wth the above algorthm, the server outputs an attenton lst of dgtal sgnage programs for an owner of the sgnage wth 1 day nterval. ISBN Feb. 19~22, 212 ICACT212

4 V. EVALUATION EXPERIMENT AND RESULTS A. Expermental Setup n a Shoppng Dstrct In order to nvestgate the practcablty and performance of the proposed system, we performed an experment n the shoppng dstrct n Ktakyushu-cty, Fukuoka, Japan for four days. Fgure 6 shows an expermental envronment of ths experment. As a feature of the dgtal sgnage n ths shoppng dstrct, the dgtal sgnage s hung at a heght of about 5 [m] on the center of the street, and only pedestrans who walk from north to south can see the contents of the dgtal sgnage. As shown n Fgure 6, n ths experment, two sensors are nstalled on both sdes of the street whose wdth s 5.5 [m]. Moreover, we counted the number of pedestrans manually, as a reference for comparson wth a sensor data. Audences of the dgtal sgnage were also counted by checkng pedestran s eyes, as a reference for the attenton estmaton. Table 1 shows an example of dgtal sgnage programs. In ths shoppng dstrct, dgtal sgnage program for an hour s confgured as one cycle, and length of the ndvdual content s typcally about 3 sec (mnmum length s 15 sec, and maxmum length s about 3 mn). Sensor2 and Server Countng audences Street 5.5[m] Dgtal sgnage Fgure 6. Expermental envronment. Sensor1 Countng pedestrans TABLE 1. AN EXAMPLE OF DIGITAL SIGNAGE PROGRAMS. From To Contents 11:: 11::3 Cafe & Bar 11::3 11:2: Moble phone shop... 11:58: 12:: Event nformaton B. Results of Pedestran Detecton Fgure 7 shows a transton of the number of pedestrans for four days. Correlaton coeffcent, between the number of pedestrans counted manually and that detected by sensor data, s.95. Therefore, the proposed system usng the ultrasonc dstance sensor can satsfactory estmate a trend of the pedestran traffc wth hgh accuracy. Number of pedestrans per 1mnute Manual count Sensor count 6/18 6/19 6/2 6/21 (11:3-18:3) (1:3-18:3) (1:3-18:3) (1:3-14:) Number Fgure 7. The number of pedestrans for four days. C. Result of Congeston Estmaton In ths evaluaton, the congeston s dvded nto three levels based on the number of pedestrans for each one mnute. The threshold to dvde the congeston level s decded from the average of the number of pedestrans and the standard devaton of that. We set a dfferent threshold between the weekday and the weekend, because the average number of pedestrans s typcally dfferent between them. In ths system, we attempt to detect the congeston level 3 of the shoppng street n partcular, because the stuaton of the level 3 means that the street becomes too crowded, and s very danger for pedestrans of the shoppng street. Therefore, we set the followng value as the threshold for dvdng the congeston level, so that the server can properly detect the congeston of ths shoppng street. C TL (4) : Threshold between the level 1 and the level 2 C TH 1. 5 (5) : Threshold between the level 2 and the level 3 where : The average of the number of pedestrans : The standard devaton of the number of pedestrans Table 2 shows a threshold of the number of pedestrans n ths shoppng street, and Fgure 8 shows an example of the threshold of the weekend. Moreover, these thresholds should be mplemented dependng on the nstallaton envronment of the system. In case of ths shoppng dstrct, the server may alert pedestrans n the shoppng street by settng thresholds of the number of pedestrans as shown n Table 2. TABLE 2. A THRESHOLD OF THE NUMBER OF PEDESTRIANS. Threshold between level 1 and level 2 Threshold between level 2 and level 3 Number of persons per 1 mnute The weekday The weekend ISBN Feb. 19~22, 212 ICACT212

5 Number of pedestrans Level 3 (Congested) Level 2 Level 1 Number of pedestrans detected by sensor Threshold of weekend 6/18 (Sat) 6/19 (Sun) Number Fgure 8. An example of congeston threshold of the weekend. D. Result of Attenton Estmaton Fgure 9 shows a probablty densty of veloctes of pedestrans n each drecton. Average velocty of pedestrans movng n vewable drecton was 3.35 [km/h], and that of pedestrans movng n non-vewable drecton was 3.69 [km/h]. Ths result ndcates that pedestrans reduce ther speed when lookng at the dgtal sgnage. Probablty densty Mean : 3.35 [km/h] Mean : 3.69 [km/h] Vewable Not Vewable Velocty [km/h] Fgure 9. A probablty densty of veloctes n each drecton. In ths experment, we manually counted the number of audences of the dgtal sgnage as a reference. Accordng to the results, we dvded dgtal sgnage programs nto two groups based on a value of an audence rate. Our purpose of attenton estmaton s to dentfy programs whch do not obtan pedestrans attenton. Therefore, 1 [%] s set as a threshold to dstngush group 1 (no attenton) and group 2 (reasonable attenton). Table 3 shows an average velocty derved from sensor data for these two groups. The result ndcates that the average velocty of the group 1 s faster than that of group 2. We conducted a t-test to clarfy a sgnfcant dfference between velocty of group 1 and that of group 2. P-value s the result of a t-test, and ndcates whether there s a sgnfcant dfference or not. From Table 3, a sgnfcant dfference was confrmed on the average velocty between group 1 and group 2 at sgnfcance level of 1 [%]. Therefore, t s possble to estmate an attenton of the dgtal sgnage nto group 1 or group 2 usng pedestran s veloctes. 7 TABLE 3. RELATIONSHIP BETWEEN VELOCITY AND AN AUDIENCE RATE Audence rate P-value to Number V Group (manually group 1 of [km/h] decded) (t-test) programs 1 % - 1% % - 5% As shown n Table 3, the number of sgnage programs of group 1 s 1, and that of group 2 s 38. Here, we exclude three sgnage programs whose length s too short and whose audence rate s too hgh as an outler. Next, we attempt to dvde an attenton of each sgnage program nto two levels based on veloctes of pedestrans who walk under the dgtal sgnage durng the program s dsplayed. Attenton level L expresses that pedestrans does not pay attenton to the sgnage programs, and corresponds to group 1 n Table 3. Attenton level H expresses that pedestrans pay attenton to the sgnage programs, and corresponds to group 2 n Table 3. The server estmates an attenton of the sgnage program s level L when the average velocty of the sgnage program s faster than a threshold of velocty. In order to accurately dvde an attenton nto two levels, we need to know a threshold of velocty so as to maxmze the estmaton accuracy. We nvestgated accuracy of the attenton by changng a threshold of velocty. Fgure 1 shows a relatonshp between estmaton accuracy and a threshold of velocty. The accuracy s an average probablty at whch the attenton level of the sgnage program s correctly decded based on the velocty of pedestrans, and s calculated from the followng equatons. n 1 P M : The accuracy of the attenton (6) n 1 where f G A then M 1 else M : Group of sgnage program at number (1 or 2) G A : Attenton of sgnage program at number (1 or 2) In the above equatons, A 1 corresponds to attenton level L, and A 2 corresponds to attenton level H. As mentoned n Table 3, the number of sgnage programs n group 2 (.e. 38) s much larger than that n group 1 (.e. 1). Therefore, for calculatng the accuracy correctly, we randomly select 1 programs from group 2 n each evaluaton. We conducted 1,5 evaluatons, and treated the average as accuracy of attenton. In Fgure 1, f a threshold of velocty s too small, the accuracy becomes 5 [%] because all sgnage programs are judged as attenton level L. Smlarly, f a threshold of velocty s too large, the accuracy also becomes 5 [%] because all sgnage programs are judged as attenton level H. 38 ISBN Feb. 19~22, 212 ICACT212

6 As shown n Fgure 1, accuracy becomes the maxmum value of 82.2 [%] when the threshold of velocty s 3.42 [km/h]. Therefore, we concluded that the threshold of velocty be set to 3.42 [km/h] n ths shoppng dstrct [%] [3] Network Applcaton Engneerng Laboratores Ltd., [4] K. Takahash, K. Nakamura and K. Yamazak, Development and evaluaton of populaton montorng network wth ultrasonc dstance sensors, IEICE Techncal Report, IA29-73, pp. 45-5, vol. 19, Dec Accuracy % mprovement [km/h] Velocty [km/h] Fgure 1. Threshold to dstngush group 1 and group 2. VI. CONCLUSIONS In ths study, we have proposed a new pedestran observaton system usng ultrasonc dstance sensors. From an expermental evaluaton of the proposed system conducted n the shoppng street, we have clarfed that correlaton coeffcent, between the number of pedestrans counted manually and that detected by sensor, s.95. Therefore, the proposed system accurately detects a trend of the pedestran traffc n ths shoppng dstrct. We have further proposed the method to estmate the congeston of ths shoppng street from the number of pedestrans. The congeston estmaton s useful for protectng safety of the shoppng street. Furthermore, we have confrmed that there s a relatonshp between an audence rate of dgtal sgnage programs and veloctes of pedestrans, and clarfed that accuracy of the attenton estmaton of sgnage programs becomes 82.2 [%] by settng an approprate threshold to veloctes of pedestrans. The attenton of dgtal sgnage programs can be utlzed for mprovng the contents of the dgtal sgnage. Snce sensor network and system can collect and store data wth 24-hour-a-day, 365-days-a-year, the long-term data about the number of pedestrans and the attenton of the dgtal sgnage wll be utlzed for vtalzaton of shoppng streets. As future works, we wll study more parameters related wth an accurate attenton of sgnage programs. ACKNOWLEDGEMENT We would lke to express sncere thanks to Ms. T. Satoh and staff of Human Meda Center of Ktakyushu-cty Authorty for ther support n ths experment. REFERENCES [1] K. Terabayash, Y. Hashmoto, Y. Hoshkawa and K. Umeda, Measurement of Pedestran n Unknown 3D Envronment Usng Subtracton Stereo, IPSJ SIG Techncal Report, 29-UBI-22, pp. 1-4, vol. 5, May. 29. [2] K. Katabra, K. Nakamura, H. Zhao and R. Shbasak An Automatc Method for Countng Pedestrans usng a Laser Range Scanner, Informaton Technology Letters, pp , vol. 4, Aug. 25. ISBN Feb. 19~22, 212 ICACT212

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