12,053 research outputs found
A multi-modal event detection system for river and coastal marine monitoring applications
Abstract—This work is investigating the use of a multi-modal
sensor network where visual sensors such as cameras and
satellite imagers, along with context information can be used to complement and enhance the usefulness of a traditional in-situ sensor network in measuring and tracking some feature of a river or coastal location. This paper focuses on our work in relation to the use of an off the shelf camera as part of a multi-modal sensor network for monitoring a river environment. It outlines our results in relation to the estimation of water level using a visual sensor. It also outlines the benefits of a multi-modal sensor network for marine environmental monitoring and how this can lead to a smarter, more efficient sensing network
Investigation into the use of satellite remote sensing data products as part of a multi-modal marine environmental monitoring network
In this paper it is investigated how conventional in-situ sensor networks can be complemented by the satellite data streams available through numerous platforms orbiting the earth and the combined analyses products available through services such as MyOcean. Despite the numerous benefits associated with the use of satellite remote sensing data products, there are a number of limitations with their use in coastal zones. Here the ability of these data sources to provide contextual awareness, redundancy and increased efficiency to an in-situ sensor network is investigated. The potential use of a variety of chlorophyll and SST data products as additional data sources in the SmartBay monitoring network in Galway Bay, Ireland is analysed. The ultimate goal is to investigate the ability of these products to create a smarter marine monitoring network with increased efficiency. Overall it was found that while care needs to be taken in choosing these products, there was extremely promising performance from a number of these products that would be suitable in the context of a number of applications especially in relation to SST. It was more difficult to come to conclusive results for the chlorophyll analysis
Integrating multiple sensor modalities for environmental monitoring of marine locations
In this paper we present preliminary work on integrating
visual sensing with the more traditional sensing modalities
for marine locations. We have deployed visual sensing at one
of the Smart Coast WSN sites in Ireland and have built a
software platform for gathering and synchronizing all sensed
data. We describe how the analysis of a range of different
sensor modalities can reinforce readings from a given noisy,
unreliable sensor
Complexity adaptation in H.264/AVC video coder for static cameras
H.264/AVC uses variable block size motion estimation (VBSME)
to improve coding gain. However, its complexity is significant
and fixed regardless of the required quality or of the
scene characteristics. In this paper, we propose an adaptive
complexity algorithm based on using the Walsh Hadamard
Transform (WHT). VBS automatic partition and skip mode
detection algorithms also are proposed. Experimental results
show that 70% - 5% of the computation of H.264/AVC is required
to achieve the same PSNR
Fast intra prediction in the transform domain
In this paper, we present a fast intra prediction method based on separating the transformed coefficients. The
prediction block can be obtained from the transformed and quantized neighboring block generating minimum distortion
for each DC and AC coefficients independently. Two prediction methods are proposed, one is full block search
prediction (FBSP) and the other is edge based distance prediction (EBDP), that find the best matched transformed
coefficients on additional neighboring blocks. Experimental results show that the use of transform coefficients
greatly enhances the efficiency of intra prediction whilst keeping complexity low compared to H.264/AVC
Low computational complexity variable block size (VBS) partitioning for motion estimation using the Walsh Hadamard transform (WHT)
Variable Block Size (VBS) based motion estimation has
been adapted in state of the art video coding, such as
H.264/AVC, VC-1. However, a low complexity H.264/AVC
encoder cannot take advantage of VBS due to its power consumption
requirements. In this paper, we present a VBS partition
algorithm based on a binary motion edge map without
either initial motion estimation or Rate-Distortion (R-D)
optimization for selecting modes. The proposed algorithm
uses the Walsh Hadamard Transform (WHT) to create a binary
edge map, which provides a computational complexity
cost effectiveness compared to other light segmentation
methods typically used to detect the required region
Using dempster-shafer theory to fuse multiple information sources in region-based segmentation
This paper presents a new method for segmentation of images into large regions that reflect the real world objects present in a scene. It explores the feasibility of utilizing spatial configuration of regions and their geometric properties (the so-called Syntactic Visual Features [1]) for improving the correspondence of segmentation results produced by the well-known Recursive Shortest Spanning Tree (RSST) algorithm [2] to semantic objects present in the scene. The main contribution of this paper is a novel framework for integration of evidence from multiple sources with the region merging process based on the Dempster-Shafer (DS) theory [3] that allows integration of sources providing evidence with different accuracy and reliability. Extensive experiments indicate that the proposed solution limits formation of regions spanning more than one semantic object
Using the discrete hadamard transform to detect moving objects in surveillance video
In this paper we present an approach to object detection in surveillance video based on detecting moving edges
using the Hadamard transform. The proposed method is characterized by robustness to illumination changes
and ghosting effects and provides high speed detection, making it particularly suitable for surveillance applications.
In addition to presenting an approach to moving edge detection using the Hadamard transform, we
introduce two measures to track edge history, Pixel Bit Mask Difference (PBMD) and History Update Value
(H UV ) that help reduce the false detections commonly experienced by approaches based on moving edges.
Experimental results show that the proposed algorithm overcomes the traditional drawbacks of frame differencing
and outperforms existing edge-based approaches in terms of both detection results and computational
complexity
Interactive object contour extraction for shape modeling
In this paper we present a semi-automatic segmentation approach suitable for extracting object contours as a precursor to 2D shape modeling. The approach is a modified and extended version of an existing state-of-the-art approach based on the concept of a Binary Partition Tree (BPT) [1]. The resulting segmentation tool facilitates quick and easy extraction of an object’s contour via a small amount of user interaction that is easy to perform, even in complicated scenes. Illustrative segmentation results are presented and the usefulness of the approach in generating object shape models is discussed
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