Traditional and recent approaches in background modeling for foreground detection: An overview

Phenology is the study of recurring life-cycle events, classic examples being the flowering of plants and animal migration. Phenological responses are increasingly relevant for addressing applied environmental issues. Yet, challenges remain with respect to spanning scales of observation, integrating observations across taxa, and modeling phenological sequences to enable ecological forecasts in light of future climate change. Recent advances that are helping to address these questions include refined landscape-scale phenology estimates from satellite data, advanced, instrument-based approaches for field measurements, and new cyberinfrastructure for archiving and distribution of products. These breakthroughs are improving our understanding in diverse areas, including modeling land-surface exchange, evaluating climate–phenology relationships, and making land-management decisions.

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Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century.

Visual sensor networks have emerged as an important
class of sensor-based distributed intelligent systems,
with unique performance, complexity, and quality of service
challenges. Consisting of a large number of low-power camera nodes,
visual sensor networks support a great number of novel
vision-based applications. The camera nodes provide information
from a monitored site, performing distributed and collaborative
processing of their collected data. Using multiple cameras in the
network provides different views of the scene, which enhances
the reliability of the captured events. However, the large amount
of image data produced by the cameras combined with the
network's resource constraints require exploring new means
for data processing, communication, and sensor management.
Meeting these challenges of visual sensor networks requires
interdisciplinary approaches, utilizing vision processing, communications
and networking, and embedded processing. In this
paper, we provide an overview of the current state-of-the-art in
the field of visual sensor networks, by exploring several relevant
research directions. Our goal is to provide a better understanding
of current research problems in the different research fields of
visual sensor networks, and to show how these different research
fields should interact to solve the many challenges of visual sensor
networks.

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A Survey of Visual Sensor Networks

As new fabrication and integration technologies reduce the cost and size of micro-sensors and wireless interfaces, it becomes feasible to deploy densely distributed wireless networks of sensors and actuators. These systems promise to revolutionize biological, earth, and environmental monitoring applications, providing data at granularities unrealizable by other means. In addition to the challenges of miniaturization, new system architectures and new network algorithms must be developed to transform the vast quantity of raw sensor data into a manageable stream of high-level data. To address this, we propose a tiered system architecture in which data collected at numerous, inexpensive sensor nodes is filtered by local processing on its way through to larger, more capable and more expensive nodes.We briefly describe Habitat monitoring as our motivating application and introduce initial system building blocks designed to support this application. The remainder of the paper presents details of our experimental platform.

Habitat monitoring: Application driver for wireless communications technology

A method is provided for modifying an image. The method comprises displaying an image, the image comprising a portion of an object; and determining if an edge of the object is in a location within the portion. The method further comprises detecting movement, in a member direction, of an operating member with respect to the edge. The method still further comprises moving, if the edge is not in the location, the object in an object direction corresponding to the detected movement; and modifying, if the edge is in the location, the image in response to the detected movement, the modified image comprising the edge in the location.

Information processing apparatus, information processing method and program

Environmental monitoring applications present a challenge to current background subtraction algorithms that analyze the temporal variability of pixel intensities, due to the complex texture and motion of the scene They also present a challenge to segmentation algorithms that compare intensity or color distributions between the foreground and the background in each image independently, because objects of interest such as animals have adapted to blend in Therefore, we have developed a background modeling and subtraction scheme that analyzes the temporal variation of intensity or color distributions, instead of either looking at temporal variation of point statistics, or the spatial variation of region statistics in isolation Distributional signatures are less sensitive to movements of the textured background, and at the same time they are more robust than individual pixel statistics in detecting foreground objects They also enable slow background update, which is crucial in monitoring applications where processing power comes at a premium, and where foreground objects, when present, may move less than the background and therefore disappear into it when a fast update scheme is used Our approach compares favorably with the state of the art both in generic low-level detection metrics, as well as in application-dependent criteria

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Background Subtraction on Distributions

A sensor node detects a plurality of information-based events. The sensor node determines whether at least one other sensor node is an information neighbor of the sensor node based on at least a portion of the plurality of information-based events. The information neighbor has an overlapping field of view with the sensor node. The sensor node sends at least one communication to the at least one other sensor node that is an information neighbor of the sensor node in response to at least one information-based event of the plurality of information-based events.

Information-based self-organization of sensor nodes of a sensor network

We present a background model that differentiates between background motion and foreground objects. Unlike most models that represent the variability of pixel intensity at a particular location in the image, we model the underlying warping of pixel locations arising from background motion. The background is modeled as a set of warping layers, where at any given time, different layers may be visible due to the motion of an occluding layer. Foreground regions are thus defined as those that cannot be modeled by some composition of some warping of these background layers. We illustrate this concept by first reducing the possible warps to those where the pixels are restricted to displacements within a spatial neighborhood, and then learning the appropriate size of that spatial neighborhood. Then, we show how changes in intensity/color histograms of pixel neighborhoods can be used to discriminate foreground and background regions. We find that this approach compares favorably with the state of the art, while requiring less computation.

Warping background subtraction

We present a scalable end-to-end system for vision-based monitoring of natural environments, and illustrate its use for the analysis of avian nesting cycles. Our system enables automated analysis of thousands of images, where manual processing would be infeasible. We automate the analysis of raw imaging data using statistics that are tailored to the task of interest. These “features” are a representation to be fed to classifiers that exploit spatial and temporal consistencies. Our testbed can detect the presence or absence of a bird with an accuracy of 82p, count eggs with an accuracy of 84p, and detect the inception of the nesting stage within a day. Our results demonstrate the challenges and potential benefits of using imagers as biological sensors. An exploration of system performance under varying image resolution and frame rate suggest that an in situ adaptive vision system is technically feasible.

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Heartbeat of a nest: Using imagers as biological sensors

We present a scalable end-to-end system for vision-based monitoring of a biological phenomenon. Our system enables automated analysis of thousands of images, where manual processing would be infeasible. We automate the analysis of raw imaging data using statistics that are tailored to the task of interest, the study of avian behavior during nesting cycles. The system uses simple image statistics (features) as the low-level representation to be fed to generic classifiers and final inferences exploit the temporal and spatial consistencies. Our testbed achieves bird detection accuracy of 82%, and egg counting accuracy of 84%, allowing inference of avian nesting stage with accuracy within a day. Our results demonstrate the challenges and potential of using imagers as biological sensors. An exploration of system performance under varying image resolution and frame rate suggest that an in situ adaptive vision system is technically feasible.

Teresa Ko

Papers

Background Subtraction on Distributions

Information-based self-organization of sensor nodes of a sensor network

Warping background subtraction

Heartbeat of a nest: Using imagers as biological sensors

Heartbeat of a Nest: Using Imagers as Biological Sensors