Functionally Layered Video Coding for River Surveillance

Iwahashi, Masahiro; Udomsiri, Sakol

doi:10.1109/icccn.2007.4317970

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…Adaptive sensing is implemented for a river monitoring application in [3]; only components useful for determining water level are transmitted from the sensor. [4] describes a method of interpolating missing Discrete Cosine Transform (DCT) coefficients of images.…”

Section: Related Workmentioning

confidence: 99%

Interleaving Wavelet Coefficients for Adaptive Data Transmission from Pervasive Sensing Systems

Panangadan

Talukder

2011

2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN)

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The paper describes a method for adaptive transmission of data over error-prone wireless links and suited to the limitations of embedded pervasive sensing systems. The technique performs a multiresolution wavelet transform on the data followed by interleaving the resulting coefficients among the transmitted packets. The interleaving scheme is designed to facilitate estimation of coefficients lost during transmission by minimizing the correlation between elements in one packet. A correlation model between detail coefficients obtained from a wavelet transform of Gauss-Markov processes is used to estimate the optimal distribution of coefficients to packets. The interleaving ensures that packet loss results in noncontiguous "gaps" in the reconstructed wavelet tree. The original data is reconstructed by polynomial interpolation of the missing coefficients from correlated neighboring coefficient. We present simulation results that show the performance of our scheme on both real-world and simulated datasets.

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Section: Related Workmentioning

confidence: 99%

Interleaving Wavelet Coefficients for Adaptive Data Transmission from Pervasive Sensing Systems

Panangadan

Talukder

2011

2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN)

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“…However, these methods have the problem that they cannot accurately measure the water level if sediment exists on the measured points. There is also the problem that the cost of installing the equipment with large wireless communication sensors is high [4]. As another solution to monitoring river optical properties, a method using a turbidity meter has been proposed [5].…”

Section: Introductionmentioning

confidence: 99%

“…To solve these problems, methods have been proposed for measuring the water level by capturing images of rivers [4,[8][9][10][11]. These methods have the merit that the cost of locating the equipment is low because only general surveillance cameras are needed.…”

Section: Introductionmentioning

confidence: 99%

“…These methods have the merit that the cost of locating the equipment is low because only general surveillance cameras are needed. In fact, although water level gauges are not likely to be located in small rivers [4], surveillance cameras can be easily located in small rivers, as well as major rivers [12]. Therefore, by analyzing images obtained by a surveillance camera, it is possible to monitor the appearance of rivers without the need for special sensors such as water level gauges or turbidity meters.…”

Section: Introductionmentioning

confidence: 99%

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River state classification combining patch-based processing and CNN

et al. 2020

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This paper proposes a method for classifying the river state (a flood risk exists or not) from river surveillance camera images by combining patch-based processing and a convolutional neural network (CNN). Although CNN needs much training data, the number of river surveillance camera images is limited because flood does not frequently occur. Also, river surveillance camera images include objects that are irrelevant to the flood risk. Therefore, the direct use of CNN may not work well for the river state classification. To overcome this limitation, this paper develops patch-based processing for adjusting CNN to the river state classification. By increasing training data via the patch segmentation of an image and selecting patches that are relevant to the river state, the adjustment of general CNNs to the river state classification becomes feasible. The proposed patch-based processing and CNN are developed independently. This yields the practical merits that any CNN can be used according to each user’s purposes, and the maintenance and improvement of each component of the whole system can be easily performed. In the experiment, river state classification is defined as the following problems using two datasets, to verify the effectiveness of the proposed method. First, river images from the public dataset called Places are classified to images with Muddy labels and images with Clear labels. Second, images from the river surveillance camera in Nagaoka City, Japan are classified to images captured when the government announced heavy rain or flood warning and the other images.

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A Critical Review on Water Level Measurement Techniques for Flood Mitigation

Jan

2021

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

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Functionally Layered Video Coding for River Surveillance

Cited by 3 publications

References 2 publications

Interleaving Wavelet Coefficients for Adaptive Data Transmission from Pervasive Sensing Systems

Interleaving Wavelet Coefficients for Adaptive Data Transmission from Pervasive Sensing Systems

River state classification combining patch-based processing and CNN

A Critical Review on Water Level Measurement Techniques for Flood Mitigation

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