This paper reports our initial work on the extraction of en-

vironmental information from images sampled from a camera deployed to monitor a river environment. It demonstrates very promising results for the use of a visual sensor in a smart multi-modal sensor network

Diamond, Dermot

O'Connor, Edel

O'Connor, Noel E.

Smeaton, Alan F.

Ó Conaire, Ciarán

English

This paper reports our initial work on the extraction of en-\ud
vironmental information from images sampled from a camera deployed to monitor a river environment. It demonstrates very promising results for the use of a visual sensor in a smart multi-modal sensor network

Name not available

River Water-level Estimation Using VisualSensingEdel O’Connor, Ciara´n O´ Conaire, Alan F. Smeaton, Noel E. O’Connor,Dermot DiamondCLARITY: Centre for Sensor Web Technologies,Dublin City University, Dublin 9, IrelandAbstract. This paper reports our initial work on the extraction of en-vironmental information from images sampled from a camera deployedto monitor a river environment. It demonstrates very promising resultsfor the use of a visual sensor in a smart multi-modal sensor network.1 IntroductionWater management is an important part of the monitoring of the natural envi-ronment and includes monitoring the water quality of coastal and inland marinelocations. Our belief is that the use of visual sensing can help to overcome someof the problems associated with in-situ wireless sensor networks [3] and providecontext to what is being sensed. It can help lead to a smarter sensing environmentwhereby resource-constrained sensors can be used more efficiently. In essence, webelieve that the singular use of any of these devices is not sufficient for effec-tive coastal monitoring and that the development of a smart multi-modal sensornetwork will lead to a more robust and effective environmental sensing system.In this paper, we report our initial work on using visual sensing to monitor ariver environment. We demonstrate its use in the estimation of water-level fromimages sampled from a camera. However, from a broader perspective, the over-all objective of our work is to investigate how visual sensing from cameras andsatellite imagers can be used as part of a multi-modal sensor network alongsidetraditional in-situ wireless sensor networks.2 Related workIn previous studies, camera sensor systems have been identified as effective toolsfor coastal monitoring. A prime example of this is a major European researchproject entitled CoastView [2] which demonstrates the use of fixed video remotesensing systems to partially ameliorate some of the problems associated within-situ measurements of waves, currents, and morphological change. However,Davidson et al. [2] point out that despite the potential to improve monitoringof coastal zones with coastal video systems, that there are many coastal man-agement issues that may only be addressed adequately through the integrationof additional data sources and expert knowledge alongside the image data. Weare investigating the integration of image data with additional sensor sources, asthe overall long-term objective of this work. Other studies have also investigatedthe use of imagers not only in the context of monitoring a marine environmentbut also in other forms of environmental monitoring applications such as formonitoring avian nesting behaviour[1] and for phenological monitoring [4]3 Water-level estimationIn our work, an AXIS PTZ Network camera was deployed overlooking the banksof the river Lee at the Tyndall Research Institute, Cork, Ireland [5] . The Smart-Coast multi-sensor system for water quality monitoring is deployed at this loca-tion [6]. Various problems were identified with the in-situ sensors and we believethe camera represents a low-cost smart sensing device that can provide contex-tual or alternative information to that provided by the in-situ sensors. A pilotstudy was undertaken whereby we exploit the appearance of rocks along thebanks of the water to provide a rough estimation of water level. The approachwe have developed for water level estimation is suitable for any marine environ-ment where the water-land boundary is visible, without interference on the thesite in question. The amount of rock visible in the images was divided into 5classes, from no-rock-visible (high water level) to high-rock (low water level).Wecaptured 2800 daylight images in July 2008 for training and used 80 imagesfrom September and February as our test set.This allows the performance of thesystem to be tested for other time periods demonstrating diverse environmentaland river conditions. We investigated three classifiers, for determining the classof an image which are described in the following section.Fig. 1. Examples of the challenging image data we are using, demonstrating disparateappearance due to varying river conditions.3.1 ClassifiersEach classifier works in a similar manner; using statistical (data) models rep-resenting each class, a query image is classified by computing the similaritybetween the image to each each class model and the most similar class is se-lected. Classification algorithim 1 (C1) used a Gaussian model for classification.A pixel-based log-likelihood (LL) model was computed using hue and saturationvalues from the training data Given an image, an LL coefficient is calculatedfor each pixel based on their hue and saturation values. A class-based Gaussianmodel was built from the mean and standard deviation image of LL values usingtraining images for each class. We compute the similarity between a test image,I, and class models, {M1, ..,M5}, using:S(I,Mk) =1NN∑i=1e−0.5× (I(i)−µk(i))2σ2k(i) (1)where µ and σ are the pixel mean and standard deviation in the LL image models(top 2 rows of figure 2) and N is the number of pixels. The second classificationalgorithm (C2) is similar to C1 except it uses normalized cross correlation (NCC)to calculate the similarity between the query image and the class models Thefinal classification algorithm (C3) uses a different statistical model to representeach class than C1 and C2. For each class, we computed the mean image usingthe raw pixel values. A test image is then classified by selecting the class withthe highest NCC value to the test image (see figure 2). We also investigated theuse of texture features, however they did improve results. We intend in futureto use more advanced classifiers such as SVMs.1 2 3 4 5Fig. 2.Models used by the the third classifier for each of the 5 water-level classes. Eachof these images represents the mean image (raw pixel mean) for its respective class.4 Experimental Results4.1 Overall accuracyThe best performing classifier was C3, classifying 75% of the test images cor-rectly. This is quite good considering the subjective nature of the depth-classhuman annotation and the fact that the test images originate from differenttimes periods demonstrating extremely diverse conditions (See figure 1). We seea more detailed analysis of the results in Figure 3, which shows the confusionmatrices for the classifiers we investigated. When classifier C3 does not correctlyestimate the class of an image, it is clear that it is usually quite close, since mostof the test images lie close to the diagonal of the confusion matrix. It also hasvery high accuracy at classifying the more extreme events (i.e. no-rock and highrock) which is promising since these tend to be more distinguishable than theother levels even to the human eye.5 ConclusionsIn this paper we developed and evaluated algorithms for classifying river im-ages to identify the water depth, in order to investigate the viability of using aC1Estimated Class1 2 3 4 51 58 9 2 11 02 1 1 65 13 03 0 1 49 23 74 0 0 1 47 325 0 0 0 48 32C2Estimated Class1 2 3 4 51 74 5 1 0 02 5 57 18 0 03 2 1 72 5 04 21 6 12 34 75 17 2 2 3 56C3Estimated Class1 2 3 4 51 74 6 0 0 02 6 55 12 4 33 1 2 62 13 24 0 1 8 33 385 2 0 1 1 76Fig. 3. Confusion matrices for the three classifiers that were evaluated, illustrating thechallenging nature of the testing data.low-cost visual sensing device for environmental monitoring. We found that thebest performing classifier used a mean image as the model and NCC for classi-fication.It performed quite well considering the challenging image data and theuse of test images from alternative seasons to the training set. We believe thatthe use of visual sensing holds great promise as a low-cost complementary sens-ing modality. Its use in the development of a smart multi-modal sensor networkforms part of our future work.Acknowledgements: Based on research funded by the Dept. of Communica-tions, Marine & Natural Resources under the Strategy for Science, Technol-ogy and Innovation (2006-2013) and by Science Foundation Ireland under grant07/CE/I1147.References1. S. Ahmadian, T. Ko, S. Coe, M. Rahimi, S. Soatto, M. Hamilton, and D. Estrin.A vision system to infer avian nesting behavior. Technical report, University ofCalifornia, Los Angeles, 2007.2. M. Davidson, M. V. Koningsveld, A. de Kruif, J. Rawson, R. Holman, A. Lamberti,R. Medina, A. Kroon, and S. Aarninkhof. The coastview project: Developing video-derived coastal state indicators in support of coastal zone management. CoastalEngineering, 54(6-7):463 – 475, 2007.3. D. Diamond, S. Coyle, S. Scarmagnani, and J. Hayes. Wireless sensor networks andchemo-/biosensing. Chemical Reviews, 108(2):652–679, 2008.4. E. A. Graham, E. M. Yuen, G. F. Robertson, W. J. Kaiser, M. P. Hamilton, andP. W. Rundel. Budburst and leaf area expansion measured with a novel mobilecamera system and simple color thresholding. Environmental and ExperimentalBotany, 65(2-3):238 – 244, 2009.5. E. O’Connor, A. F. Smeaton, N. E. O’Connor, and D. Diamond. Integrating multiplesensor modalities for environmental monitoring of marine locations. In SenSys ’08,pages 405–406, New York, NY, USA, 2008. ACM.6. B. O’Flynn, R. Martinez-Catala, S. Harte, C. O’Mathuna, J. Cleary, C. Slater,F. Regan, D. Diamond, and H. Murphy. Smartcoast: A wireless sensor network forwater quality monitoring. In SenseApp ’07, pages 1853–1856, Sep 2007.

River water-level estimation using visual sensing

DCU Online Research Access Service

A vision system to infer avian nesting behavior.

Budburst and leaf area expansion measured with a novel mobile camera system and simple color thresholding. Environmental and Experimental Botany,

Integrating multiple sensor modalities for environmental monitoring of marine locations.

Smartcoast: A wireless sensor network for water quality monitoring.

The coastview project: Developing videoderived coastal state indicators in support of coastal zone management.

Wireless sensor networks and chemo-/biosensing.

http://doras.dcu.ie/14795/1/Edel_EuroSSC09.pdf

River water-level estimation using visual sensing

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