3D Single Point Imaging Technology for Tracking Multiple Fish

Mohammadmehdi Saberioon, Petr Cisar and Jan Urban

Laboratory of Image and Signal Processing, Institute of Complex Systems, Faculty of Fisheries and Protection of Waters,

University of South Bohemia in České Budějovice, Zámek 136, 37333, Nové Hrady, Czech Republic

Keywords: Fish Tracking, 3D Single Point Imaging, Kinect, Trajectory.

Abstract: Image based tracking like video tracking has shown potential in aquaculture behavioural studies in past

decade. Image based tracking is allowing to have higher spatial and temporal resolution in compared to most

conventional methods such as hand scoring, tagging and telemetry. They also permit to have more information

about the environment rather than other methods. Most studies about trajectory are based on tracking in two

dimensional (2D) environments; however, organisms are mostly included in three dimensional (3D)

environments which influence ecological interactions extensively. Furthermore, in 2D image analysis,

occlusion of fish is a frequent problem for analysis of fish tracking and ultimately their behaviour. Recently,

new hardware based on single point 3D imaging technology have been developed which can provide 3D

single points in real-time by combining a colour video camera, infrared video camera with an infrared

projector. The main objective of this study was to develop a multiple fish tracking system in 3D space based

on the current available 3D imaging technology. Developed system could accurately (98%) track multiple

Tilapia (Oreochromis niloticus) which was freely swimming in an aquarium. This study contributes to

feasibility of new sensors to monitor fish behaviours in 3D space.

1 INTRODUCTION

Aquatic organisms are very sensitive to alterations in

the aquatic environment and they respond to changes

with distinctive movement and behaviour (Little and

Finger, 1990; Mancera et al., 2008). Monitoring of

fish behaviour such as individual food intake and

swimming speed not only can provide useful

information for improving production management

(Oppedal et al., 2011; Brown et al., 2006), but also it

would help farmers to observe fish behaviour as a

welfare indicator (Zion, 2012). Fish behaviour

analysis can also be used for environmental risk

assessment, like examining presence of chemical

agents in water as an inexpensive and fast alternative

to laboratory analysis (Kane et al., 2004; Masud et al.,

2005; Xiao et al., 2015).

During past decade, machine vision system

(MVS) as an alternative to conventional method, has

been used for real-time and offline monitoring of fish

behaviours such as schooling and shoaling (Salierno

et al., 2007; Suzuki et al., 2003), novelty behaviours

(Stewart et al., 2012), abnormality behaviours (Beyan

and Fisher, 2013; Pinkiewicz et al., 2011), feeding

behaviours (Parsonage and Petrell, 2003; Lee et al.,

2013; Atoum et al., 2015) and respond behaviours to

stress which caused by high stocking density

(Papadakis et al., 2012) or hypoxic condition (Xu et

al., 2006). In other words, MVS can provide

automate, inexpensive, non-invasive and accurate

information about fish behavioural parameters

(Delcourt et al., 2012).

To date, a number of MVSs have been developed

for studying individual fish behaviours. For instance,

Kato et al., (2004) developed a system for quantifying

individual Zebrafish (Brachydanio rerio, Cyprinidae)

behaviours such as velocity, swimming distance,

trace map and turning directions. Stewart et al. (2012)

recorded individual fish behaviours in open filed test

arenas to understand the fish novelty behaviours.

Papadakis et al., (2012) developed low cost MVS to

analysis fish behaviours in tank. However, tracking

and monitoring multiple fish using MVS

automatically have also been studied. For instance,

Pinkiewicz et al., (2011) developed a system to record

and analysis multiple fish movement using Kalman

filter and data association techniques based on video

footage of salmon in sea cage. Mirat et al., (2013)

expanded a program called ZebraZoom to track

larvae’s Zebrafish movement. Perz-Escidero et al.,

Saberioon, M., Cisar, P. and Urban, J.

3D Single Point Imaging Technology for Tracking Multiple Fish.

DOI: 10.5220/0005634001150121

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 4: BIOSIGNALS, pages 115-121

ISBN: 978-989-758-170-0

115

(2014) developed a visual system called idTracker for

tracking individuals, even siblings, in a group based

on finding fingerprint of each animal in a video

recording of a group. Lee et al., (2014) developed a

MVS based on particle filter algorithm for tracking

multiple fish in a single tank.

Most studies about trajectory are based on

tracking in two dimensional (2D) environment;

however, most organisms are included in three

dimensional (3D) environment, which influence

ecological interactions extensively (Pawar et al.,

2012). For instance, it would be difficult to recognize

some behaviours that contain vertical movements

(Horodysky et al., 2007). Moreover, occlusion is a

frequent problem for analysis of fish tracking and

ultimately their behaviour where 2D images are using

for analysis. Therefore, tracking animal in their 3D

environment is more desired as a promised method in

animal behavioural studies.

Tracking system in 3D has studied using different

approaches such as light field video cameras which

composite optics are used to simultaneously capture

images focused at different distance from lens,

therefore allowing to reconstruct scene in 3D

(Matsumoto et al., 2013), or single image of

reflections or shadows from surface of a 3D surface

such as spherical mirror (Kanbara et al., 2006; Chen

et al., 2011). Nevertheless, multiple cameras are

usually employed to reconstruct 3D scene for tracking

objects. For example, Spitzen et al., (2013) used two

monochrome couple- charged device (CCD) video

cameras to study in-flight behaviour of individual

malaria mosquito to human odor and heat in 3D

space. Cachat et al., (2011) reconstructed 3D

environment using images from two video cameras,

and manually recorded position of individual

Zebrafish to understand neurophenotyping of adult

Zebrafish in 3D environment. Some other researchers

also tried to track multiple objects using multiple

cameras. Viscido et al., (2004) used stereo vision

system to track 4 to 6 group of giant danios (Danio

aequipinnatus). Veeraraghavan et al., (2006)

proposed a method based on motion information to

track multiple bees using two video cameras. Wu et

al., (2011) developed an algorithm by solving three

linear assignment problems for tracking multiple fruit

fly using two video cameras automatically.

Synchronizing multiple cameras usually need

different hardware and complicated software; they

also need more handlings, which may affect animal

behaviours (Dell et al., 2014). Besides, spatial

resolution of images are dramatically drops when

objects move away from sensors (Gokturk et al.,

2004).

Recently, new hardware based on single point 3D

imaging technology (e.g. Microsoft Kinect or Asus’s

Xtion Pro) have been developed. This hardware can

provide 3D single points in real-time by combining a

colour video camera, infrared video camera with an

infrared projector to create defined infrared laser light

pattern which depth information can be obtained.

These new hardware practically provide possibility to

develop an affordable tracking system to study

multiple fish in real-time. So, the main objective of

this study was to develop a multiple fish tracking

system in 3D space based on the aforementioned

sensor. To the best of our knowledge, no research has

been done on examining this technology for fish

tracking. The introduced system was able to resolve

the occlusion problem and track each fish separately

even the siblings in real-time 3D space.

2 MATERIALS AND METHODS

2.1 Experimental Setup

The experiment took place at Laboratory of Signal

and Image Processing of Institute of complex

systems, FFPW, University of South Bohemia, Czech

Republic. Tilapia (Oreochromis niloticus) was

selected for demonstrative purpose in this study.

Tilapia has been used in many studies as the

experimental model for behavioural studies because

of its well characterized responds to stress (Moreira

and Volpato, 2004). Standard length (SL), between

the front head extremity and the insertion of the tail

fin, and body height (BH), in front of the first ray of

the dorsal fin, as morphometric parameters were

measured. Fish selected for this study had 8.6, 8.4, 8,

8 cm SL and 2.6, 2.5, 2.5, 2.4 cm BH respectively.

The glass aquarium (60 cm ×30 cm ×29 cm, 10 cm

water depth) with transparent sides was used for

recoding fish activities. In order to avoid water

surface movements that could create light reflection,

Microsoft Kinect v1 was placed under the tank from

a stationary (70 cm) in centre of the aquarium and

vertical position, which sensor was faced to the

aquarium. This distance was selected to maintain the

most field of view (FoV) of Microsoft Kinect v1,

which is 43° vertically and 57° horizontally and

increase the depth resolution (Khoshelham and

Elberink, 2012).

Tilapia is changing its skin colour quickly based

on ambient colour in background, which causes low

contrast between fish and background. Therefore, in

order to have high contrast between fish and

background for better post processing such as

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

116

segmentation, Blue Bristol board was used as

background (Xu et al., 2006).

Video was recorded for 30 minutes using software

which has been developed especially for this study by

authors with maximum sample rates of 10 frames per

seconds (fps) and then converted to image sequence

for further processing. Four 30-minute video

sequences that recorded one, two, three and four

individuals were totally prepared. The Experimental

aquarium was indirectly lit by two lamps, which

provide low light intensity.

RGB Images from the Microsoft Kinect v1 were

recorded in portable network graphics (PNG) format

with spatial resolution of 480×640 pixel. Images have

three components (namely red, green and blue) with

each colour comprising 256 graduations. Depth raw

data were also recorded in 11-bit format resolution.

Figure 1 shows the schematic of recording setup. The

depth images are constructed by triangulation from IR

image and the projector and carried by IR image. In

other words, a 3D coordinate point of depth images

) is constructed from the measurement of [x, y,

z] in depth image as below, which has been described

by Smisek et al., (2011):



































,





(1)

Where K

is Infrared camera calibration, k

distortion parameter of the IR camera, c

and c

are

parameters of the model, dis is distortion, u

and v

are mean value of shift position from IR to Depth

camera (u

= 3.0 and v

= 2.9), and are projected to

the RGB image as below:

RGB

= K

RGB

dis (R

RGB

– C

RGB

), k

RGB

) (2)

where dis is distortion function, k

RGB

is distortion

parameter of the RGB camera. R

RGB

is the rotation

matrix, K

RGB

is calibration matrix and C

RGB

is the

centre of RGB camera.

Figure 1: Schematic of single point 3D multiple tracking

system.

2.2 Pre-processing

Microsoft Kinect’s RGB images were converted to

Hue, Saturation, and Value (HSV) colour model

using method described by Tang et al., (2003). HSV

colour model provides immunity to illumination

condition. Once after the conversion, segmentation

process was performed to remove unwanted pixels.

Moreover, some morphological operations such as

erosion and dilation have been performed to eliminate

the segmentation noises. All of the images were

processed using Matlab Image processing toolbox.

2.3 Image Processing

Real world X and Y of centroid of multiple tilapia

were obtained using the method proposed by Pérez-

Escudero et al., (2014). The method is a multi-

tracking algorithm based on extracting of

characteristic fingerprint from each object in a video

recording of a group, from RGB images. First, a set

of the reference images was extracted, which fish

were separated. Then, algorithm compared reference

images with images, where fish were connected. To

obtain clear fingerprint for each fish, intensities of

two pixels (I

, I

) separated in d distance, were

determined in each frame, and then were used to

identify each fish in all frames. Moreover, algorithm

aggregated the information of all images that belong

to the same individual, while it moved without

crossing with any other individual. This will improve

the probability of correct identification of each

individual. Depth information (z-value) was also

extracted from Kinects’ depth image; first converting

resolution of images from 11 bit to 8 bit, then

transformed to 3D point cloud using Matlab image

processing toolbox. Finally, fish trajectories, were

extracted based on 3D location of each fish in each

frame.

2.4 Accuracy Assessment

The proposed system is acquiring the trajectory

automatically, thus it is important to assess the

accuracy of the result respectively. Accuracy and

precision of the system was evaluated by comparing

results with ground truth. Human inspection was used

to evaluate 2D track produced by using finger

printing method frame by frame, and then associating

the 3D position between each consecutive frames to

form complete trajectory.

3D Single Point Imaging Technology for Tracking Multiple Fish

117

3 RESULTS AND DISCUSSION

The proposed system was applied to four 30-minute

video sequences that were recorded one, two, three

and four individuals. Figure 2 shows the original and

pre-processing image frame. Fish were successfully

segmented in most frames from the background. Any

remained noise pixels, which remained, were

removed using morphological operations.

(a)

(b)

(c)

Figure 2: Experiment images and processing results, (a)

original image from Microsoft Kinect in RGB bands, (b)

segmented image and (c) segmented image after correction.

Before constructing trajectory in 3D space,

initially we first tracked individual fishes in 2D space

in each frame using Pérez-Escudero et al., (2014)

proposed method. Depth information (z-value) from

each individual was extracted after tracking fish in 2D

space using both systems. Figure 3 shows the

trajectory of four fish in 3D space using both sensors.

Proposed system was successfully tracked

multiple fish with 98% accuracy. The obtained 3D

coordinate estimation accuracy was similar to the

accuracy that could be obtained from stereo vision

type systems as convention system for determining

3D coordinates (Torisawa et al., 2011). However,

decreasing in accuracy of the proposed tracking

system is expected with increasing in number of

tracking objects. The results of this study indicated

that the single point 3D imaging technology could be

employed for fast, accurate, inexpensive and non-

invasive multiple fish tracking even under sever

occlusion. The proposed system added length of third

dimension (z-value) for not only improving the

precision but also for shedding light on animal

activity in 3D environment which were the limitation

for previous systems (Delcourt et al., 2006).

Figure 3: 3D Trajectory acquisition results of four fish from

a 10-minute video using in 3D.

Single point 3D imaging offers information with

less computational and power consumption, which

make it ideal technology for monitoring in real-time

with less handling process. However, current

available single point 3D imaging hardware have

some limitations which make them difficult to use in

commercial scale. For instance, Khoshelham and

Elberink (2012) pointed out for tracking and mapping

object(s) using Kinect with high resolution, the data

should be acquired within 1-3 m distance to the

sensor, which makes remote sensing application of

sensor to 3 meter. The number of animals which can

be tracked using Kinect is also limited to how many

fish will be fitted in the FoV of the camera (Schramm,

2010).

4 CONCLUSIONS

This study developed an automatic system for

accurate tracking of multiple fish in 3D environment

even under severe occlusion. The system was based

on fingerprinting that track individual fish in 2D

space and depth information which was provided by

depth sensor in Microsoft Kinect. The results of the

system evaluation showed that the introduced system

was able to track multiple fish in an aquarium even

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

118

under sever occlusion successfully. However, current

available 3D single point imaging system has some

limitations but it would be expected to be used in near

future as an ideal sensor for monitoring and recording

animal behaviour in 3D space in real-time when study

is conducted in small environments such as

aquariums.

Proposed system could be extended further for

studying fish behaviours by evaluating more states

such as speed and inter-individual spacing. It can also

be used to study individual fish behaviour in 3D space

in a group which would provide useful information

about fish schooling. It would be necessary to

understand the maximum number of fish which can

be tracked using the introduced system, which future

research may need to answer this question.

ACKNOWLEDGEMENTS

The authors acknowledge the financial support of the

Ministry of Education, Youth and Sport of the Czech

Republic- projects ‘CENAKVA’ (No.

CZ.1.05/2.1.00/01.0024),'CENAKVA II' (No.

LO1205 under the NPU I program).

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