DEVELOPMENT AND TESTING OF AN ULTRASONIC-BASED PEDESTRIAN COUNTING SYSTEM

Lesani, Miranda-Moreno, Fu, Romancyshyn

1

DEVELOPMENT AND TESTING OF AN ULTRASONIC-BASED 1

PEDESTRIAN COUNTING SYSTEM 2

3 4

Asad Lesani, Ph.D. student (Corresponding author) 5 Department of Civil Engineering and Applied Mechanics, McGill University 6 Room 492, Macdonald Engineering Building, 817 Sherbrooke Street West 7 Montreal, Quebec H3A 2K6 8 Tel: +1 (514) 473-4292 9 Email: [email protected] 10

11

Luis F. Miranda-Moreno, Ph.D., Associate Professor 12 Department of Civil Engineering and Applied Mechanics, McGill University 13 Room 268, Macdonald Engineering Building, 817 Sherbrooke Street West 14 Montreal, Quebec H3A 2K6 15 Tel: +1 (514) 398-6589 16 Fax: +1 (514) 398-7361 17 Email: [email protected] 18

19

Ting Fu, Ph.D. student 20 Department of Civil Engineering and Applied Mechanics, McGill University 21 Montreal, Quebec H3A 2K6 22 Tel: +1 (514) 398-6589 23 Email: [email protected] 24

25

Taras Romancyshyn, Undergraduate Research Assistant 26 Department of Civil Engineering and Applied Mechanics, McGill University 27

Room 391, Macdonald Engineering Building, 817 Sherbrooke Street West 28 Montreal, Quebec H3A 2K6 29

Email: [email protected] 30 31 32

Word count 33 Text 4467

Tables ( X 250) 1

Figures ( X 250) 11

Total 7467

34 Paper prepared for presentation at the 94nd Annual Meeting of the Transportation Research Board, 35

January 2015 36 37

mailto:[email protected]




2

ABSTRACT 1

This paper introduces the components, hardware and software, of a pedestrian counting 2

system based on ultrasonic sensor technology. The proposed counting system is designed to 3

collect data in different facilities such as sidewalks, trails, and building corridors. To evaluate the 4

performance (accuracy) of the proposed system, manual video-based counting was obtained in 5

different traffic conditions. In addition, the ultrasonic system was compared with an existing 6

infrared-based sensor currently available in the market. The results show that the counting error 7

of the ultrasonic sensor was typically between 0 to 15% for different traffic conditions. 8

Correction error functions were also fitted which can help to improve counting accuracy. The 9

comparative analysis between the ultrasonic and infrared systems indicates that the system 10

performs better in situations with high pedestrian volumes. In particular, the sensor performance 11

was tested on sidewalk placed in an open space because this situation presents a challenge for 12

most infrared-based counting systems. The results show that the system is accurate in these 13

conditions. The proposed system seems to have the potential for counting pedestrian in different 14

facility types (including crosswalks), weather conditions and for counting cyclists in bicycle 15

facilities. 16

Key words: Ultrasonic Sensor, Automated Pedestrian Counts, High Volumes, Alternative 17

Technologies 18

19

1. INTRODUCTION 20 Non-motorized transportation is an area of growing interest in research. New 21

considerations in the planning, designing and operation of pedestrian facilities have increased the 22

need for pedestrian traffic (activity) data and consequently technologies and methods for 23

automatically collecting such data. Pedestrian data is necessary for several different reasons 24

such as for evaluating the effectiveness of the design and construction of new facilities, for 25

defining extrapolation (expansion) factors to estimate average annual daily volumes as well as 26

for monitoring walking pattern over a long-term and evaluating the impact of policies, etc. 27

28

Traditional pedestrian count data collection methods used by cities and municipalities rely 29

heavily on manual counting procedures conducted by individuals in the field. That is, they 30

require that individuals manually count the number of pedestrians using a facility, either directly 31

at the site or from video recordings of the area. Obviously, these methods require a great deal of 32

time and resources. In addition, the temporal coverage is very limited because only a few hours 33

of counts are typically obtained in one day (e.g., 4 to 8 hours of counts). As one can imagine, 34

short-term manual counts can be very sensitive to temporal (time of the day, day of the week and 35

season) and weather conditions. To standardize short-term counts taken in different periods of 36

time and weather conditions, adjustments factors have been developed based on long-term 37

counts. Because of this and other data considerations, data collection over long periods of time is 38

becoming a more and more common practice in many cities around the world. Accordingly, 39

automatic data collection for gathering pedestrian counts is becoming increasingly common. It is 40

therefore not surprising to understand the increase in effort and amount of resources currently 41

being invested in the development of large-scale automated pedestrian (and bicycle) counting 42

programs in North America. Today, several cities are either improving or launching such 43

programs, with budgets that range in the hundreds of thousands of dollars per year. These cities 44


3

and metropolitan areas include Vancouver, Ottawa, San Francisco, Portland, San Diego, and 1

many others. 2

For automatic data collection, several technologies have been developed over the past 3

decade; some of them are available for commercialization, including passive infrared counters, 4

radar, depth (3D) counters, laser-based counters, and computer vision based counters. The 5

advantages and drawbacks of these technologies have been documented in the literature review. 6

For instance, infrared sensors have some advantages such as relative ease to build and operate, 7

very low power consumption, real time implementation, acceptable accuracy and independency 8

to wet and foggy weather which make them very flexible and easy to install for long periods of 9

time. However, some of the limitations of this technology include an occlusion problem when 10

installed in environments with high pedestrian volumes or pedestrians walking in groups, low 11

performance under extreme temperatures, etc. (Greene-Roesel et al., 2008). Also, one of the 12

challenging issues with this type of sensor is that it works best when there is an obstruction such 13

as a wall on one side of sidewalk. In this paper this infrared sensor is tested in a location without 14

the presence of wall. Some sensors of this type are already available in the market and are 15

offered by several companies such as Eco-Counters. 16

On the other hand, while advances in computer vision allow for the collection of pedestrian 17

flows in open spaces including sidewalks and intersections, no current video technology can 18

collect data for long periods of time in an automatic fashion. In addition, video sensors in low-19

light conditions and inclement weather may perform poorly. Despite the current developments in 20

the research, there still exists a need to develop low-cost and flexible data collection systems that 21

are able to provide pedestrian volumes in semi real-time and integrate automatic data into a 22

platform for analysis or data fusion. 23

The main objective of this research was to develop a data collection system that 24

automatically detects and counts pedestrians based on the emission of ultrasonic waves. This 25

paper reports the system elements and algorithm used for pedestrian detection and counting. As 26

part of this research, the performance of the sensor was evaluated and compared to one of the 27

most popular commercially available types of sensor – infra-red sensors. The limitations and 28

advantages of the system are discussed along with plans for future work. 29

30

2. LITERATURE REVIEW 31 Several technologies are available in the market and used in practice for automatic 32

pedestrian data collection in indoor and outdoor environments. Typically, collection devices are 33

used to detect and count the number of pedestrians passing through doors, indoor corridors in 34

terminals or buildings, park trails, sidewalks, cycle tracks, intersection crosswalks, mid-block 35

crossings, etc. An important gamma of technologies exists such as passive infrared sensors, laser 36

scanning, pressure pads, thermal sensors, and video camera sensors. These advantages, which 37

include the need to allocate much less time and resources to the data collection effort, as well as 38

a significant reduction in the incidence of errors, are discussed by Ismail et al. (2009). 39

In order to benefit from the advantages offered by automated data collection, transportation 40

agencies must install one or several different commercially available pedestrian counters. These 41

counters, along with other methods currently in development, are presented and discussed in 42

work by Markowitz et al., 2009 and Raghuram et al., 2002. The most popular types of automated 43

pedestrian detection devices and techniques include passive and active infrared technology, 44

piezoelectric pads, laser scanners, as well as video-image processing. A more expensive, but 45

very accurate alternative is the use of thermal imaging cameras (Leykin and Hammound, 2006). 46


4

From the available counting technologies, infrared sensors appear to be the most popular 1

type of sensor for counting in outdoor environments. Passive infrared counting technologies 2

identify and count pedestrians based on a temperature differential. Among the advantages of 3

infrared sensors available in the market, one can mention that they are easy to install on existing 4

adjacent infrastructure on sidewalks and open spaces. Additionally, they have a low-energy 5

consumption rate that allows them to function for several years without requiring to be 6

recharged. Infrared detectors are ideal for pedestrian counting on streets with low to moderate 7

traffic volumes. However, several limitations can also be associated to this technology. As is the 8

case with most current technologies, occlusion problems generated from high pedestrian volumes 9

and group occurrences can lead to counting errors. In particular, the performance of this 10

technology can deteriorate in environments with very high pedestrian volumes per hour (e.g., 11

greater than 500 pedestrians per hour). Cases of under-counting and over-counting have also 12

been observed and attributed to certain temperature anomalies and weather conditions (rain). 13

Ideally, the device must be facing a fixed object such as a wall or fence opposite the sidewalk on 14

which it is mounted. This restricts their use at intersections, midblock crosswalks, open spaces, 15

etc. (Greene-Roese et al., 2008) 16

Some recent studies have investigated the performance (accuracy and precision) of infrared 17

sensors in different environments and sidewalks. Studies have reported a systematic under-18

counting error ranging between 0 and -25% depending on the traffic volumes and weather 19

conditions (Greene-Roese et al., 2008). 20

Alternative technologies such as radio waves, laser pulses, thermal sensor, etc. are less 21

popular in practice than infrared counters. Radio waves sensors work based on the Doppler 22

effect. These sensors work based on the variation of the wave frequency of the received and sent 23

signal. The variation of the frequency has a linear relationship with the speed of the object. 24

A laser pulse system works based on scanning the detection area of the sensor and 25

producing a three dimensional image of the object. Based on the video processing algorithm, the 26

speeds and volumes can be calculated. Both radio waves and laser pulses are used to determine 27

the characteristics of objects in the detection area. Among the limitations of laser scanners, one 28

can mention the high computational requirements and high acquisition costs. 29

Thermal sensors can be mounted above entryways to count people entering and exiting key 30

locations. In a recent study (Ozbay et al., 2010) tested a thermal sensor on trails and compared 31

the results with that of an infrared sensor. The authors reported mean percentage errors ranging 32

from -15% to 1% for the thermal sensor, which was considerably lower than the errors ranging 33

between -28% and 0% for the infrared sensor. With the important progress on image processing 34

algorithms and computing technologies being made, the use of computer vision has attracted 35

attention in the field of active transportation and has been proposed for counting pedestrians and 36

other road users. The main benefit of using computer vision is that they can obtain counts, speeds 37

and trajectories of mode types. The drawbacks of video-based systems are their high installation 38

and maintenance costs and the fact that they can only be used if an optimal camera placement is 39

feasible. Existing video-based techniques perform poorly at night or in adverse weather. It 40

should also be noted that video-based systems in transportation are still overwhelmingly reliant 41

on post-processing rather than real-time processing of video data. This makes large-scale data 42

collection more costly and cumbersome, as videos must be regularly collected from installed 43

cameras. 44

Regarding research done on using devices emitting ultrasonic waves to detect and count 45

pedestrians in urban environments, very little has been done to this day. While ultrasonic sensors 46


5

have been used in different areas in the field of intelligent transportation systems (Leibe et al., 1

2005) (Krammer et al., 2006), they seem to have been skipped over when it comes to automated 2

pedestrian detection and counting. This research is an effort towards the development of low-cost 3

counting systems that provide higher levels of accuracy in critical conditions such as high 4

volumes and open spaces.. 5

6

3. SYSTEM COMPONENTS 7 In this section, the structure of the designed ultrasonic-based system is described. Its design 8

involves two aspects: hardware design and software design. Hardware design includes 9

integrating all necessary elements to have a real-time automated pedestrian counting system. The 10

methodology of how pedestrian counts can be obtained from the raw output of the ultrasonic 11

sensor is described in the software design section. 12

13

3.1 Hardware Design 14 The hardware of the designed system consists of three different parts: a sensory system, a 15

microprocessor (which is the core of the system) and a robust data logger system. All of the three 16

different parts are explained in this subsection. 17

18

Sensory System 19

An ultrasonic sensor is used to measure the distance between the sensor and passing 20

objects. The ultrasonic sensor works based on the concept of the ultrasound wave speed in the 21

air. The sensor transmits an ultrasound wave in the air for a predefined time (around 5 micro 22

seconds) and the listens to the echo of the wave. Based on the transmitting and receiving time 23

difference and knowing the speed of sound wave in the air, the distance from objects can be 24

measured. 25

The ultrasonic sensor has some advantages in comparison with other technologies in the 26

market designed to measure distance. In particular, the transmitter and receiver of the ultrasonic 27

sensor can be mounted on the same board. Also, because the ultrasound wave is independent of 28

weather condition and environment light, the accuracy of the measurement should be reliable 29

regardless of the weather conditions even in hot and sunny summer days; the condition that 30

affects the performance of the infrared-based sensors the most. 31

32

Microprocessor 33

The microprocessor is the most important part of the system hardware. The microprocessor 34

can be programmed in some programing languages. In this project, an AVR based micro 35

controller is used to implement the pedestrian counting methodology and interface all other parts 36

of the system. Additionally, the pedestrian counting method can be relatively easy implemented 37

with this type of micro controller. 38

39

Data Logger 40

The data logger consists of the following parts: 41

i) Real Time Clock Module, to keep the data time stamped 42

ii) GSM modem, to keep the system working in real-time and send data to the server every 15 43

minutes 44

iii) SD card, to save all the data on SD card in case the GSM modem internet connection is lost 45


6

It is worth mentioning that once the system components are built, they are integrated in a 1

solid water-proof enclosure. Figure 1.a provides a picture of the system hardware. Moreover, 2

Figure 1.b shows the sensor with the enclosure installed at one of the testing sites. 3

4

(a) System hardware

(b) Installed sensors: infrared and ultrasonic

Figure 1. System hardware and installation 5

6

7

3.2 Software Design 8 The methodology and necessary steps to implement an automated pedestrian counting are 9

described in this section. 10

11

Step 1 - Distance Measurement 12

In this step, the micro controller actuates the ultrasonic sensor to send the ultrasound wave 13

to the environment for a duration of 5 micro seconds. After this time, the processor listens for the 14

echo of the ultrasound wave and based on the time difference between transmitting and receiving 15

the ultrasound wave and its speed, the distance between the sensor and a passing object is 16

measured. 17

18

Step 2 - Noise Reduction Measurement 19

In some cases the ultrasonic sensor cannot get the ultrasound wave echo of the object in the 20

meantime. Therefore, the distance to the object is measured as the maximum range of the sensor 21

(e.g., 3. meters for this sensor, it can be changed by the user such as to cover a limited area like 22

at crosswalks). To solve this issue, an algorithm is required to eliminate this noise from the 23

output of the system. The one used in this system is based on the moving average concept. At 24

first, a window of samples is defined that should be used to calculate the average for them. This 25

window size should be defined considering the sample rate, average speed of a pedestrian and 26

average width of the pedestrian passing coverage area (from trial and error the 5 last samples 27

were considered). Since the miscalculation of the distance is always the maximum range of the 28

sensor, a minimum value of the samples in that window is used instead of the average. Figure 2 29

shows an example of noisy data and the output from the noise reduction technique. 30

31


7

1 Figure 2. Noisy distance measurement and filtered data 2

3

Step 3 - Object Detection 4

In this step each distance measurement is classified based on the distance thresholds. When 5

the processor gets a new measurement it checks the distance value with the value of the last 6

sample. If the difference is more than predefined threshold (e.g., 35 cm) then a new class of 7

distance is started. The mentioned threshold was considered based on the average body width of 8

the people. If a smaller value is obtained, things like bags, or even a moving hand of a person can 9

be considered as a new group. On the other hand, if it is considered to be bigger, two people 10

passing in parallel with the small distance cannot be detected as two separate groups. 11

When a new distance group is started then the mean value of all distance samples in last 12

group is calculated. Also, the number of the distance measurement samples in each class is 13

counted for future processing. 14

Figure 3 shows an example of the distance measurements for a few seconds when two 15

pedestrians passed the sensor sight line. As depicted in Figure 3, the object detection process in 16

cases when objects (pedestrians) pass one by one is straightforward. But in some cases objects 17

such as pedestrians walking their bikes or pedestrian hands and bags can be considered as a new 18

class. The average value of the distance samples in this type of class will be very close to the 19

average of the previous or next class but, the number of samples will be much smaller than the 20

sample class belonging to the pedestrian body. So, some thresholds can be defined on the 21

average value and the number of the samples in each class of measurement to eliminate over-22

counting. 23


8

1

2 Figure 3. Sample of distance measurements for two objects 3

4

In addition to the above mentioned issue, there are situations in which pedestrians pass side 5

by side without any detectable gap between them. Figure 4 illustrates this situation. To be able 6

detect these patterns (objects walking almost in parallel), another threshold for the mean value 7

for each sample class needs to be defined. As explained before, each sample class refers to a 8

group of samples that are in close proximity to each other. So, if the differences between the 9

mean values of two subsequent sample classes are more than the threshold value, the second 10

object is detected as new object. 11

12

13 Figure 4. Samples of measurements with two pedestrians passing almost in parallel 14

15

Step 4 - Decision Making 16

In this step based on the defined rules on the mean values and number of distance samples 17

in each class, the algorithm decides whether or not the passing object is a pedestrian. If the rules 18

0

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are satisfied, the object is counted as a pedestrian and the counter value is increased. In order to 1

have a better view of the pattern in each class the decision making is done based on the 2

information from the last four classes. This information helps to check if the two subsequent 3

patterns with close distance mean values belong to the same pedestrian or two separate 4

pedestrians. 5

After doing so, the processor gets the time from the real-time clock module and sends the 6

counter value to the server every 15 minutes (it can be changed by user). Then, the counter value 7

is reset to zero. 8

Figure 5 provides the flow chart of the pedestrian detection methodology described above. 9

10

11

12

Figure 5. Flow chart for pedestrian detection methodology 13 14

15

3.3 Performance measures 16 17

In order to evaluate the performance of the system,, the following testing protocol was 18

applied: 19

Star

t

Send ultrasonic wave & listen

to the echo

Does Sample belong to the

last class?

[abs(d(i) - d(i-1)) > 35]

Measure distance to object

Run noise reduction algorithm Increase counter

Compute mean value of

distances in the last

measurement class

Mean values and the

number of observations

meet thresholds?

No

Yes No

Yes


10

Selection of Testing Sites: In this case sidewalks with different volume intensities as well 1

as different pedestrian flow patterns such as people walking in groups, were the subjects of 2

interest. 3

Sensor installation: Sensors were installed on existing infrastructure (posts). In parallel 4

with the sensor, a video camera on a mast was installed. In one of the testing locations, an 5

infrared sensor (pyro box contact) from Eco-Counter (http://www.eco-compteur.com) was also 6

installed. 7

Data Processing and Analysis: After several hours of data collection with the two systems 8

and the video camera, counts for every 15-min interval were obtained for each sensor. Video 9

data was processed manually and defined as the “ground truth”. For this purpose, plots and error 10

measures were obtained. The error measures include: 11

For each time interval i the error value is defined as

. Then, the classical 12

Average Percentage Deviation (APD), computed as

∑

, where Ai is the 13

automated count for time i (15min in this case), and Gi is the manual count for time period i. 14

Dispersion between automatic counts and manual (video) counts was also measured using the 15

Absolute Percentage Deviation (AAPD):

∑ |

|

. 16

17

4. SYSTEM EVALUATION 18 This section presents the approach that was used to evaluate the performance of the 19

ultrasonic-based counting system using the protocol defined previously. As a first step, three 20

different sites with different widths and volume conditions were selected. For each site, one or 21

two days of data collection were conducted from which video data was also recorded to obtain 22

manual counts. These 3 sites are described as follows: 23

24

4.1 Site selection 25

26 Site 1 - Milton Street – site with pedestrians

walking in groups. The ultrasonic sensor was

first installed on Milton Street, in Montreal,

at a site located a few blocks east of McGill

University, on the southern sidewalk. This

location was chosen because of its relatively

high flow of pedestrians, most of which are

university students either heading to or away

from the university. It is not difficult to

observe pedestrian walking in groups, which

is a condition that is critical for automatic

counting with traditional sensors such as

infrareds. Figure 6 shows a snapshot of site

1.

Figure 6. Snapshot of site 1

http://www.eco-compteur.com/


11

Site 2 – Sherbrooke Street – site without a

wall. The second site was located on

Sherbrooke Street West, near the main gate of

McGill University, Montreal. The sidewalk

was selected because of its high pedestrian

traffic as well for its lack of a wall in the

proximity. The width of the sidewalk is more

than the first site and there is no wall on one

side of the sidewalk. A limitation of an

infrared sensor is that sidewalks in which the

sensor is installed should have a wall or a

clear physical-delimitated area. Open spaces

or inexistence of a wall can deteriorate the

performance of this type of sensor. Figure 7

shows a snapshot of site 2.


Site 3 – University Street – site with stop-and-

going flow conditions. The third site was

located at the intersection if University Street

and Saint Catherine. This site was selected

because of its high pedestrian traffic given its

location close to a commercial area and for

the retail exhibit windows that cause stop-

and-going flow conditions. Pedestrians often

stop to look at retail exhibits causing over-

counting issues. However this site has a well-

defined counting area with a wall. Figure 8

shows a snapshot of site 3.


1

2

4.3 Results 3 The general results of the three sites are presented in Table 1. From this table, one can 4

observe that the errors percentage varied between 0.9 and 24.7 for the ultrasonic sensor and 5

between 0.8 and 42.1 for the infrared sensor over the 3 test sites. In addition, the average AADP 6

for the ultrasonic and infrared sensors ranged from 6.4 to 12.3 and 4.6 to 19.4, respectively. 7

These results clearly show that the Ultrasonic sensor performed better. 8

9

10

11

12

13

14

15


12

1

2

Table 1. Summary statistics of tests per site 3

Measure Site 1 Site 2 Site 3

Error (%)

Infrared Min - Min 0.9 Min 0.8

Max - Max 42.1 Max 9.9

Ultrasonic Min 1.7 Min 3.7 Min 0.9

Max 24.7 Max 18.6 Max 27.2

ADP (%)

Infrared - -19.4 -0.5

Ultrasonic -11.3 -9.9 2.8

AADP (%)

Infrared - 19.4 4.6

Ultrasonic 12.3 9.9 6.4

4

5

Figures 9a and 9b show the automated and manual counting values (46 intervals of 15-6

min counts) for the first site. Figure 9.a shows the manual versus (vs) the automatic ultrasonic 7

counts from which an R2 of 0.88 was obtained. Figure 9.b shows the error measures. As 8

expected, an under-counting problem was observed, which increased slightly as volumes 9

increased. The system over-counted pedestrians in only one case. 10

11

Figure 10.a and 10.b show the results for the second site (Sherbrooke). This site had 12

slightly greater volumes than Milton. Moreover, automated pedestrian counts from both Eco-13

Counter infrared and ultrasonic sensors are presented and compared with respect to manual 14

counts obtained from video data. These results clearly show again the better performance of the 15

ultrasonic system compared than the infrared system. The ultrasonic system presents much lower 16

errors and a higher R2 measure. Also, the results of site 2 show error values of more than 25% 17

several times. This highlights the limitation of the infrared based sensor in sidewalks that are 18

located in open areas. The infrared sensor needs an obstruction, such as wall, or well-defined 19

detection area to have accurate counting. On the other hand, the error values of the counting with 20

the ultrasonic system are less than 15% most of the time. Also, one of the advantages of the 21

ultrasonic sensor is that the measurement range of the ultrasonic sensor can be limited being able 22

to be used in an open area without any obstruction. 23

24

Figure 11.a and 11.b show the result of the third site on University Street. Figure 11.a 25

shows the manual vs the automatic ultrasonic and automatic infrared sensors and Figure 11.b 26

shows the error values. From these figures, it can be seen that most of the time the error values of 27

counting with both systems lie between 0 to 12%. This shows a relatively high level of accuracy 28

in both systems, with very high R2 in both cases. However, there are some over-counting issues 29

because of the high pedestrian volumes as well as the stop-and-going pattern of the pedestrian 30


13

flow. That is, the over-counting error is because pedestrians stop for certain amounts of time in 1

front of the sensors. The over-counting error seems to be more severe in the ultrasonic sensor 2

under this stop-and-going condition. 3

4

a) Site 1. Manual vs Ultrasonic counts

b) Site 1 – Ultrasonic error

5

Figure 9. Test outcomes for site 1 6

-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

50 70 90 110 130 150 170

Counts

(%)

Volume (15-min periods)


14

1

2

3 a) Site 2. Ultrasonic vs Infrared counts 4

5 b) Site 2 – Ultrasonic vs Infrared errors 6


8


15

1

2 a) Site 3. Ultrasonic vs. Infrared counts 3

4 a) Site 2 – Ultrasonic vs Infrared errors 5



16

1

CONCLUSIONS AND FUTURE WORK 2 3

This paper introduces an original automated pedestrian counting system based on 4

ultrasonic technology. The system components, hardware and software, are described in this 5

document along with the testing protocol. Three different sites were selected to test the accuracy 6

of the counting system and its functionality in different challenging situations including high 7

volumes, large counting areas without a wall present and stop-and-going pedestrian flow 8

conditions. Video data was collected in order to obtain manual counts (defined as the “ground 9

truth”). Sites and counting periods were selected in order to observe a large variability in the 10

magnitude of counts. One of the sites had a wide sidewalk without a wall, in which traditional 11

counting technologies do not work properly. 12

13

The initial findings clearly show the potential of this counting system with a very good 14

performance on sidewalks with low to high volumes (from 200 to 800 pedestrians per hour). The 15

under-counting error, due mainly to occlusion, varies between 0 to -20% in 98% of the cases. 16

Based on the results, the error increases slightly with volumes, with a power or linear function, 17

which can be used to generate correction functions. These functions can be used to improve the 18

counting estimates (reduce error) in congested sidewalks. 19

20

The proposed system could help handling the limitations of infrared technology, such as 21

the requirement of an obstruction, such as a wall, or well-defined detection area. In open areas or 22

wide sidewalks without walls, the definition of the counting area is a challenge for infrared 23

sensors. In this condition, the accuracy of the infrared based sensor is deteriorated. In the 24

proposed ultrasonic system, there exists the option of changing the coverage area, so it can be 25

used at different locations including open spaces. Additionally, the ultrasonic technology is not 26

sensitive to temperature as opposed to an infrared based sensor which can be significantly 27

affected on hot-sunny days. 28

29

One of the main limitations of the ultrasonic sensor with respect to infrared sensor is the 30

higher energy consumption. Batteries in infrared sensors can work for several years, while in our 31

system the battery only works for a few days. In addition, there a number of interesting 32

improvements and developments that must be explored to further enhance our product. These 33

include: 34

• Modify the detection methodology to count the number of cyclists in bicycle facilities. 35

• Reduce the power consumption of the system to increase the lite time of the system 36

working on battery. 37

• Modify the system to count pedestrians based on the direction of travel (using two 38

sensors in parallel). 39

• Implementing a system with two ultrasonic sensors to increase the accuracy of distance 40

measurements and decrease measurement noises. 41

• Implement data fusion theory in order to create a system that works alongside other 42

sensors such as a combination of a Pyro-electric Infrared sensor and an ultrasonic sensor. 43

• Test the system under different weather conditions. 44

45

46


17

1

2

REFERENCES 3 1. Greene-Roesel, Diogenes, Ragland and Lindau, “Effectiveness of a Commercially Available 4

Automated Pedestrian Counting Device in Urban Environments: Comparison with Manual 5 Counts”, Safe Transportation Research & Education Center, 2008 6

2. Ismail, Karim, Tarek Sayed, and Nicolas Saunier. "Automated collection of pedestrian data using 7 computer vision techniques." Transportation Research Board Annual Meeting Compendium of 8 Papers, Washington, DC. 2009. 9

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DEVELOPMENT AND TESTING OF AN ULTRASONIC-BASED PEDESTRIAN COUNTING SYSTEM

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