Multiview Human Body Tracking of Hurdle Clearance: A Case Study

Tomasz Krzeszowski

, Krzysztof Przednowek

, Krzysztof Wiktorowicz

and Janusz Iskra

Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, Rzeszow, Poland

Faculty of Physical Education, University of Rzeszow, Rzeszow, Poland

Faculty of Physical Education and Physiotherapy, Opole University of Technology, Opole, Poland

Keywords:

110 m Hurdle Race, Human Motion Tracking, Multicamera System, Particle Swarm Optimisation.

Abstract:

This initial research is a case study that uses a multiview human body tracking method as a tool to measure

hurdle clearance kinematic parameters. This study is conducted on a hurdler representing a high sport level,

who is a participant in the European and World Championships and the Olympic Games. The video recordings

were made under simulated starting conditions of a 110 m hurdle race. Kinematic parameters are estimated

based on the analysis of images from a multicamera system. The images were recorded with a resolution

of 1920x1080 and with a frequency of 100 Hz. The proposed method does not use any special clothes,

markers or other estimation support techniques. The parameters of the hurdle clearance were compared with

the parameters obtained from ground truth poses. Mean Absolute Error and Mean Relative Error were used as

the quality criteria.

1 INTRODUCTION

Hurdling is a group of athletic events in which tech-

nical preparation is very important. The hurdle race

technique involves running over 10 hurdles that are

from 0.84 to 1.07 m high, depending on the event.

In these races, the technique evaluation is mainly

focused on certain phases of human motion (Iskra,

2012). The existing studies are devoted to the kine-

matic analysis of the so called ”hurdle clearance”

(

Coh, 2003;

Coh et al., 2008). For example, the pa-

per by

Coh (

Coh, 2003) analyses selected kinematic

parameters (e.g., height of center of mass, angle of

placement of a leg) that describe the Colin Jackson’s

hurdle clearance technique. Meanwhile, the paper by

(Salo et al., 1997) contains a three-dimensional (3D)

biomechanical analysis of sprint hurdles. To esti-

mate the parameters (e.g., take-off distance, horizon-

tal velocity), two cameras (25 Hz) with ”Kine anal-

ysis” software were used. The main objective here

was to determine and compare selected biomechani-

cal parameters in two groups of men and two groups

of women at different competitive levels.

There are a number of computer vision methods

that play an increasingly important role in supporting

sports training. For example, (Reyes et al., 2016) de-

veloped an algorithm that processes underwater video

sequences for swimmer detection and tracking based

on light absorbance in conjunction with compres-

sive sensing concepts. The developed algorithm was

tested on two video sequences. Meanwhile, motion

detection and tracking methods have been used to

analyse athletics videos (Ramasso et al., 2009; Pana-

giotakis et al., 2006). Another solution that uses com-

puter vision techniques is a system for tracking play-

ers in indoor team games, such as in handball (Per

and Kovacic, 2000). Indoor sports have also been

analysed by (Kim and Cho, 2016), who proposed a

robust multi-object tracking algorithm for acquiring

object oriented multi-angle videos. In this algorithm,

multiple camera images are integrated using homog-

raphy based transformation in order to cover large ar-

eas of interest. A motion tracking of a tennis racket

using a markerless system with a monocular camera

has been presented by (Elliott et al., 2014). Further-

more, (Sheets et al., 2011) used a markerless motion

capture system to evaluate kinematic differences at

the lower back, shoulder, elbow, wrist, and racquet

between the ﬂat, kick, and slice serves. In this study,

seven male players were tested on an outdoor court

in daylight conditions. A method to identify sports

players in videos has been proposed by (Hamatani

et al., 2016), where the identiﬁcation was achieved by

motion feature matching between (unknown) players

in videos (the features were obtained from estimated

postures in the videos) and wearable sensors whose

Krzeszowski T., Przednowek K., Wiktorowicz K. and Iskra J.

Multiview Human Body Tracking of Hurdle Clearance: A Case Study.

DOI: 10.5220/0006498400830088

In Proceedings of the 5th International Congress on Sport Sciences Research and Technology Support (icSPORTS 2017), pages 83-88

ISBN: 978-989-758-269-1

IDs were already known. The experimental results

showed that the proposed method successfully iden-

tiﬁed 10 players with 72% accuracy. Cheng et al.

(Cheng et al., 2004) have shown that the proposed

motion descriptor can successfully classify the fol-

lowing four sports types: sprint, long-distance run-

ning, hurdling and canoeing. Their experimental re-

sults were obtained using video material from the

1992 Barcelona Olympic Games. Finally, Zhang et

al. (Zhang et al., 2017) proposed an algorithm to track

player actions from a sports video sequence. Their

method combines a particle ﬁltering and mean shift

in order to effectively trace a fast-moving target.

This study proposes a multiview markerless

method of human body motion tracking to estimate

hurdle clearance parameters. In our analysis, nine

distance parameters and eight angle parameters are

taken into account. These parameters are estimated

based on the analysis of image sequences captured

with a multicamera system. The tracking system that

we have developed does not use any special clothes,

markers or other techniques supporting estimation.

This study is a continuation of research presented in

our previous paper (Krzeszowski et al., 2016). To

the best of our knowledge, the method of multiview

markerless tracking is used for the ﬁrst time for mea-

surement of hurdle clearance kinematic parameters.

2 METHODS

2.1 Multiview Human Motion Tracking

The purpose of human body tracking is to estimate a

pose that reﬂects as closely as possible a real pose. It

should be noted that capturing the 3D position of a

human body is a very difﬁcult task (John et al., 2010;

Kwolek et al., 2012). The main problems include:

high dimensional search space, image noise, the large

variability in the appearance of the tracked humans

and environment, the complexity of human motion

and the fact that particular parts of the body are of-

ten obscured. Although these issues can be solved in

many different ways, the most common methods use

simpliﬁed human body models (Deutscher and Reid,

2005; John et al., 2010; Kwolek et al., 2012; Krzes-

zowski et al., 2016), uniform background (Deutscher

and Reid, 2005), and also properly selected clothes

of the tracked human body in order to facilitate the

determination of distinctive features. In the process

of tracking, a particle ﬁlter algorithm (Sidenbladh

et al., 2000) or its modiﬁed versions are frequently

used (Deutscher and Reid, 2005). However, these al-

gorithms require a signiﬁcant number of particles in

Figure 1: 3D human body model (left), hierarchical struc-

ture (right).

order to ﬁnd the correct solution, which directly im-

pacts on the time needed for computations. Therefore,

in the human body motion tracking process, particle

swarm optimisation algorithms (Kennedy and Eber-

hart, 1995; John et al., 2010; Kwolek et al., 2012;

Krzeszowski et al., 2016) are mostly used because

they enable a more effective exploration of the search

space.

A 3D model is used to determine the human body

pose; that is, the position and orientation in space as

well as the angles between the joints. The model that

is used in this research is based on the kinematic tree

structure consisting of 11 segments, each of which is

represented by a truncated cone (Kwolek et al., 2012;

Deutscher and Reid, 2005), see Figure 1. The space in

which the model operates is determined by the num-

ber of degrees of freedom (DoF). Each segment in-

cludes up to three DoFs that deﬁne its orientation; the

only exception is the pelvis, which contains three ad-

ditional segments deﬁning the model translation. The

model used in this paper includes 24 DoFs. In our

method both the model conﬁguration and the pose of

a human body in the ﬁrst frame of a sequence of im-

ages are selected manually.

The particle swarm optimisation algorithm (PSO)

(Kennedy and Eberhart, 1995; Krzeszowski et al.,

2016) is used in the motion tracking process. In our

method, the position of a particle represents the hypo-

thetical state (pose) of an athlete. The best solution is

selected based on a ﬁtness function value. The ﬁtness

function determines the degree of similarity between

the real and the estimated human pose. In this study,

the ﬁtness function consists of two components. The

ﬁrst is determined on the extracted human silhouette,

whereas the second is based on the edge distance map

(John et al., 2010; Krzeszowski et al., 2016). The

value of the function for the cth camera is calculated

using the following equation:

(x) = 1 − (a f

(x) + b f

(x)) (1)

where x is the human body pose (the position of a par-

ticle) and a, b are experimentally chosen weighting

factors. The f

(x) function describes the degree of

overlap of the rendered 3D model with the extracted

silhouette, and f

(x) is determined by comparing the

3D model edges with the image, including the map

with pixel distances from the nearest edge. The ﬁt-

ness function for all cameras is determined according

to the following equation:

f (x) =

∑

c=1

(x) (2)

where C = 3 is the number of cameras.

2.2 Data Acquisition

The proposed method was applied for two image se-

quences. The recorded athlete was a participant in the

European and World Championships and the Olympic

Games. The data were registered in the athletics hall

with a tartan track. Throughout the research, the

sequence of passing the third hurdle in the regula-

tion conditions of 110 m race (hurdle height: 1.067

m, distance between the hurdles: 9.14 m) was cap-

tured. The sequences, in the form of color images

of size 1920x1080, were captured with three Basler

Ace acA1920-150uc cameras with the frequency of

100 Hz. Figure 2 illustrates how the cameras were

arranged in the athletics hall. The parameters of the

cameras have been estimated using the TSAI calibra-

tion method (Tsai, 1987).

2.3 Evaluation of the Parameters

Five key points (P

–P

) were analysed in three phases

of hurdle clearance. The analysis included 17 parame-

ters, which are presented in Figure 3. The parameters

were selected based on the literature review (Iskra,

2012;

Coh, 2003). The quality of the tracking was

evaluated based on the estimated pose and the ground

truth pose. The ground truth pose was obtained by

manually matching the 3D model to the athletes on

RGB

Recording

equipment

RGB

hurdle

direction of the run

hurdle

9.14 m

1.35 m

4.03 m

1.33 m

h = 1.32 m

5.20 m

4.30 m

Figure 2: Acquisition station, h is the height of the camera

from the ground.

those images that contained the ﬁve key poses char-

acteristic for hurdle clearance (Figure 3). The error

level was determined for each parameter. The param-

eters were estimated by the implemented algorithm

and they were then compared with the values of the

ground truth reference model (the model was man-

ually adjusted to the analysed images). The quality

criterion was deﬁned for each parameter as:

= |

− X| (3)

MAE =

∑

n=1

(4)

where e

is the absolute error, N is the number of al-

gorithm repetitions,

is the estimated value (deter-

mined by the algorithm), X is the ground truth value,

and MAE is the mean absolute error. Moreover, the

mean relative error was calculated from the following

formula:

MRE =

∑

n=1

· 100 (5)

3 EXPERIMENTAL RESULTS

An example of the tracking results for the selected se-

quence for three views is shown in Figure 4. The basic

statistics for the analysed parameters, the ground truth

value X, and the errors MAE and MRE are presented

in Table 1. These results were obtained for N = 10

repetitions of the tracking algorithm. The error anal-

ysis shows that among all of the distance parameters,

the estimation of CM height over hurdle in P

) for

sequence 1 is determined with the greatest MRE er-

ror. This error is equal to 13.3% (MAE = 37.3 mm).

In the case of sequence 2, this error is smaller and

equal to 7.9% (MAE = 23.6 mm). Krzeszowski et al.

(Krzeszowski et al., 2016) used a monocular motion

tracking system, and this parameter was determined

with MAE = 30.5 mm and MRE = 8.3%. The small-

est MRE error for both sequences is obtained for the

CM distance from the hurdle in P

). This error is

equal to 0.5% (MAE = 13.5 mm) in sequence 1 and

it is equal to 0.7% (MAE = 17.3 mm) in sequence 2.

In the work by (Krzeszowski et al., 2016), the errors

for w

were MAE = 25.8 mm and MRE = 1.0%.

By analysing the angular errors it can be seen that

the smallest MRE error was obtained for the angle

of the trial leg in P

(α

) and it is equal to 2.4%

(MAE = 1.6

◦

) in sequence 1 and 4.1% (MAE = 2.9

◦

)

in sequence 2, whereas the angle of inclination of the

torso in P

(γ

) is determined with the greatest er-

ror MRE = 40.1% (MAE = 16.4

◦

) in sequence 1 and

MRE = 32.9% (MAE = 13.4

◦

) in sequence 2. In the

Figure 3: Key points and parameters of hurdle clearance: P

is the braking point in take-off phase, P

is the propulsion point

in take-off phase, P

is the center of mass (CM) over the hurdle in ﬂight phase, P

is the braking point in landing phase, P

the propulsion point in landing phase, h

is the height of CM, w

is the CM to hurdle distance, α

is the angle of the trial leg,

is the height of CM, w

is the CM to hurdle distance, α

is the angle of the trial leg, γ

is the angle of inclination of the

torso, h

is the height of CM (over the hurdle), β

is the angle of the lead leg, γ

is the angle of inclination of the torso, h

the height of CM, w

is the CM to hurdle distance, α

is the angle of the lead leg, γ

is the angle of inclination of the torso, h

is the height of CM, w

is the CM to hurdle distance, and α

is the angle of the lead leg.

Figure 4: Example of tracking results on sequence 1, frames #5, 19, 40, 57, 65; the green skeleton is the ground truth pose

and the white skeleton is the estimated pose.

work by (Krzeszowski et al., 2016), the estimation er-

rors for the mentioned parameters were MAE = 7.8

◦

and MRE = 10.1% for α

, and MAE = 5.9

◦

and

MRE = 12.1% for γ

It should be emphasised that in this paper the

errors were calculated for two particular sequences,

Table 1: Measured parameters and errors; units: h, w [mm], α, γ, β [

◦

], MRE [%].

Sequence 1 Sequence 2

Parameter ¯x sd X MAE MRE ¯x sd X MAE MRE

961.7 8.2 953.5 9.2 1.0 966.2 20.3 959.1 14.8 1.5

2513 15.1 2502 13.5 0.5 2565 14.9 2550 17.3 0.7

66.4 2.7 62.4 4.0 6.4 64.6 3.7 61.4 4.2 6.9

1155 10.1 1159 8.7 0.8 1123 11.4 1130 11.5 1.0

1541 11.7 1525 16.2 1.1 1623 15.3 1605 18.4 1.1

67.5 2.1 67.2 1.6 2.4 69.8 2.5 72.0 2.9 4.1

76.3 4.2 74.9 3.8 5.0 75.9 4.3 75.9 3.1 4.1

246.7 21.3 280.8 37.3 13.3 287.9 29.2 297.9 23.6 7.9

133.8 29.6 164.0 30.2 18.4 139.0 24.5 164.0 25.7 15.7

57.2 5.4 40.8 16.4 40.1 53.9 8.2 40.8 13.4 32.9

1176 45.5 1099 80.1 7.3 1144 30.5 1100 43.2 3.9

1128 26.3 1175 46.9 4.0 1235 29.3 1296 60.8 4.7

73.2 6.9 70.4 6.0 8.6 74.7 3.9 68.0 6.8 10.0

58.9 10.8 65.8 10.5 15.9 53.0 11.4 65.8 14.4 22.0

1118 84.9 1041 87.2 8.4 1094 40.3 1049 49.0 4.7

1670 71.1 1683 53.8 3.2 1777 52.4 1810 48.3 2.7

91.7 33.3 70.0 22.3 31.8 75.3 5.1 70.0 6.1 8.7

while in the work by (Krzeszowski et al., 2016) the

mean errors were obtained for 10 sequences.

4 CONCLUSIONS

This paper has proposed a multiview markerless

method to track human body motion. This method

was tested by experiments that were performed on

two image sequences of an athlete clearing a hurdle.

The estimated parameters were compared with the pa-

rameters obtained from ground truth poses. The er-

ror analysis indicates that the preliminary results are

promising but that further research is necessary. Con-

sequently, our further work will focus on a detailed

evaluation and improvement of the proposed method.

We will also concentrate on its use to support the tech-

nical preparation of hurdlers.

ACKNOWLEDGEMENTS

This work has been supported by the Polish Min-

istry of Science and Higher Education within the re-

search project ”Development of Academic Sport” in

the years 2016-2018, project No. N RSA4 00554.

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