Long-term underwater camera surveillance for monitoring and analysis offish populations

Bastiaan J. Boom1, Phoenix X. Huang1, Cigdem Beyan1, Concetto Spampinato3

Simone Palazzo3, Jiyin He2, Emmanuelle Beauxis-Aussalet2, Sun-In Lin4

Hsiu-Mei Chou4, Gayathri Nadarajan1, Yun-Heh Chen-Burger1, Jacco van Ossenbruggen2

Daniela Giordano3, Lynda Hardman2, Fang-Pang Lin4, Robert B. Fisher11School of Informatics, University of Edinburgh, 2Interactive Information Access, CWI, Amsterdam

3Department of Electrical, Electronics and Computer Engineering, University of Catania4National Applied Research Laboratories, Taiwan

Abstract

Long-term monitoring of the underwater environ-ment is still labour intensive work. Using underwatersurveillance cameras to monitor this environment hasthe potential advantage to make the task become lesslabour intensive. Also, the obtained data can be storedmaking the research reproducible. In this work, a sys-tem to analyse long-term underwater camera footage(more than 3 years of 12 hours a day underwater cam-era footage from 10 cameras) is described. This systemuses video processing software to detect and recognisefish species. This footage is processed on supercomput-ers, which allow marine biologists to request automaticprocessing on these videos and afterwards analyse theresults using a web-interface that allows them to displaycounts of fish species in the camera footage.

1. Introduction

In order to study the effects that climate change andpollution has on the environment, long-term monitoringof the environment is necessary. One of the most impor-tant natural environments on earth are the coral reefs,however monitoring the fish population and biodiver-sity is still a challenging task. Data collection in thiskind of environment is labour intensive, requiring diversto count the fish species in a certain area [7]. In recentyears, digital video recording has become much cheaperwhich makes underwater cameras a good alternative fordata collection. Furthermore, automatic video process-ing and pattern recognition is able to process this kindof data. This paper describes an entire system whichis been developed to allow marine biologists to analyse

large amounts of video data for long-term monitoringpurposes.To give an indication of the challenges in this project, asummary is given of the amount of data that the systemis expected to process. At the moment, around 10 cam-eras record 12 hours a day (daylight) where some havealready recording over 4 years. The estimated amountof raw video data is at the moment 112 Terabytes. Byprocessing this data using automatic video processingsoftware, we expect to find around 1010 fish, which willalso be categorized by species (or family). All this datawill be stored in a database, which is expected to takeup to 500 Gigabytes. Besides processing all this data,it will also be a challenge to present this data to marinebiologists in usable manner.The goal of this paper is to present the structure of thesystem. This system has three main challenges:

1. Processing the data, both the raw videos process-ing and querying based data extracted from thesevideos

2. Dealing with the uncertainty in the data. This un-certainty is caused by mistakes during detectingand recognizing the fish.

3. Visualization of the data, where the results are sta-tistically correct, verifiable and reproducible.

2. Literature Review

Several studies [7] have been performed in marinebiology where the fish population is counted using vol-unteer divers. This has several drawbacks in compari-son with underwater camera surveillance. Firstly, somefish species will hide themselves if divers appear, while,in the case of camera surveillance, the fish get used tothe cameras. Secondly, the observation performed by

96 CPU super computer

FishRecogition

FishDetection

Workflow

Interface

VIPDatabase

VideoDatabase

Figure 1: A schematic overview of the systemsoftware components and the two databases

the volunteers is not always very stable, because boththe region covered and ability to recognise species canvary. Finally, the observations of the volunteers are notreproducible.In recent years, underwater cameras have been usedto study coral reefs [8]. Several approach have beendeveloped to detect and recognise fish using underwa-ter camera footage, but this work is mainly restrictedto more constrained environments [13, 9, 5, 4]. Thispaper uses an improved version of the methods devel-oped by Spampinato et al [10], where both fish detec-tion, tracking and recognition are performed on videofootage recorded in corals. In [10], one of the largestexperiments used 320 fish image to verify the perfor-mance of their methods. Since our goal is to run on 1010

fish, a larger dataset is obtained to verify our automaticvideo processing software.

3. Overview of System

The system can be divided into four software compo-nents and two databases as shown in Figure 1. The soft-ware components are the fish detection, fish recogni-tion, user interface and workflow. These software com-ponents run on supercomputers at NARL, which pro-vides the architecture to record the videos and store andprocess the data. At the moment, both the fish detectionand recognition is running distributed on a 96 CPU su-percomputer in Taiwan and the VIP (Video/Image Pro-cessing) database is filled with fish which are located inthe video and afterwards the species of the fish is deter-mined. The user interface is web-based, connection tothe database allowing the user to, for instance, visualizethe fish count at certain time intervals. In the followingsection, more details of the different software compo-nents are given.

3.1. Workflow

The workflow component has to dynamically gen-erate the commands to invoke the fish detection and

Figure 2: An example of fish detection andtracking

recognition software based on requests from marine bi-ologists (called workflow composition) and schedulethem on the distributed platform/supercomputer usinga resource scheduler. It is common for computer visionmethods to have some parameters which can be tunedfor different environments/fish species. The workflowperforms reasoning based on semantics [6] to find thecorrect parameter set. In addition, some parameterscannot be set until the execution of another task is com-plete, causing the workflow to have to separately man-age the composition and scheduling phases in order tocontrol task and data dependencies. The system is stillunder development so newer versions of the softwareare sometimes released, however to keep the result ver-ifiable and reproducible older versions of the softwareare still supported. In the interface, marine biologistswill be able to request the processing on different videosand the workflow gives an estimate of the running time.At the moment, the workflow is used to process histori-cal (previously recorded) videos.

3.2. Fish Detection and Tracking

The fish detection component both finds the fish inthe image and follows the fish as time progresses usingtracking algorithms. The first version of this softwareis described in [10]. The current system has multiplebackground subtraction methods, which can deal withdifferent underwater conditions. After the detection,tracking of the fish is performed (both shown in Fig-ure 2) using covariance based fish tracking [12], whichgives the trajectory for each fish throughout the video.Assuming that behavior is related to the trajectories ofthe fish, a study has analysed the behaviour of the fishduring typhoons [11].

3.3. Fish Recognition and Clustering

The fish recognition component assigns to each fisha species label [2]. At the moment, a vector of descrip-tors is extracted using the color, contour and texture in-formation of the fish. In order to classify the fish basedon the descriptors, we are using a hierarchical classifier.

Figure 3: 15 most common fish species in theunderwater camera footage

Fish species are organised in a hierarchical manner bybiologists and we use a classifier with a similar struc-ture. Forward feature selection is used to select a subsetof all features, while support vector machines are usedas the classifier at each node of the tree. At the moment,the fish recognition software is able to recognise the 15fish species shown in Figure 3.

3.4. Interface

The interface allows the marine biologist to ask cer-tain queries to the system. Based on several interviewsconducted with marine biologists, a list of twenty ques-tion has been developed with typical questions whichcan be expected from a marine biologist. Examplesof these question are: “What populations live in areaX?”, “What predator-prey relationship can be observedin area X?”, etc. Lots of these questions are related tocounting the fish species in the camera footage. A typ-ical interface for this is shown in Figure 4(a). Howeverit is also important that the marine biologist can verifythe results of the different components and can changesome of the settings based on observations of the data.For this reason, the interface allows users to focus onspecific aspects of the data, and check which compo-nents are responsible for processing the video and fordescribing the fish appearing the video (e.g., species ortrajectory of fish). The interface also allows marine bi-ologists to share the information with each other, allow-ing other marine biologists to check the data.

4. Verification of the System

The verification of the system is very important andthe user interface helps marine biologists to perform vi-sual verification, however it is also important to mea-sure the performance of automatic video processing

(a) Interface showing the fishpopulation for the month ofApril

(b) Interface to annotate thefish location, contour andtrajectories in a video

(c) The first interface to re-move images that do notbelogn to the clusters

(d) The second interface tolink the image in the toprow to a label

Figure 4: Interfaces

software. The basic idea is that if we have a measure ofaccuracy in fish detection and recognition on a couplevideos, we can extrapolate this accuracy on all videos.In order to determine the accuracy, the data needs tobe annotated by humans. The annotation process in thecase of fish detection is very different from that of thefish recognition. Both processes are briefly discussed inthe following sections.

4.1. Annotations for fish detection and tracking

For fish detection, three important aspects can beevaluated: the boundingbox, contour and trajectoryof the fish. A tool was developed for this kind ofgroundtruth annotation [3], shown in Figure 4(b). Au-tomatic algorithms are used to give suggestions aboutthe boundingbox, contour and tracking, however theuser can change these suggestions. For the bounding-box, the background subtraction methods are used. Theinitial contour is also given by the background subtrac-tion method, but other automatic methods like grabcutand snakes can be used to improve the contour if incor-rect. The tool allows the user to easily show consecutiveframes which makes it possible to verify the trajectoryof the fish.

4.2. Annotations for fish recognition

Annotation of the fish species based on the detectedfish is more difficult. The reason is only marine biolo-gists might know the species name given a fish image.However, the marine biologists are not going to anno-

tate thousands of fish images. To overcome this prob-lem the methodology in [1] was used to perform annota-tion based two interfaces (Figure 4(c) and 4(d)). Firstlyclustering is performed obtaining clusters of similar fishimages, where we have one representative image foreach cluster (mean of cluster for instance). The firstinterface in Figure 4(c) allows users to label if the fishbelongs to the same species as the representative im-age. The second interface (Figure 4(d)) allows usersto link this representative image to a species, whereeach species is represent by a typical image of thatspecies. Marine biologists are then only asked aboutnew species. This annotation framework has the ad-vantage that it is faster and requires almost no domainknowledge.

5. Current Status

The system is not yet fully operational, but so farwe have detected fish on approximately 958 hours (2 1

2months) of video, resulting in 2819257 detected andtracked fish and 205 hours of video are processed byfish recognition software. Performance analysis using aground truth of 35935 detected fish shows a detectionand tracking rate of 79.8% with a 11.8% false detectionrate. 20074 images of fish have been annotated by atleast 2 persons, however because of low resolution andmurky water only a smaller subset of 6874 annotatedfish containing 15 species is used to evaluate the fishrecognition algorithm that achieves an average recall of83.15%±7.10. The user interface is still under develop-ment, but is already able to run some simple statisticson the fish stored in the VIP database (see Figure 1).The system currently uses 96 processors and is process-ing previously recorded videos of a single camera at therate of ±4 hours per day.

6. Conclusion

A research tool is described in this paper to monitorthe fish population in a coral reef environment byanalysing massive amounts of video footage. This isachieved by automatic video processing software andthe supercomputer facilities in Taiwan. Groundtruthdata is in place to measure the performance of auto-matic video processing software. The user interface isbeing developed to allowing marine biologists to accessthe data and reprocess video with different parametersettings in order to verify hypotheses.

Acknowledgements: This research was fundedby the European Commission (FP7 grant 257024)and the Taiwan National Science Council (grant

NSC100-2933-I-492-001) and undertaken in theFish4Knowledge project (www.fish4knowledge.eu).

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