CamSim:a distributed smart camera network simulator

Smart cameras allow pre-processing of video data on the camera instead of sending it to a remote server for further analysis. Having a network of smart cameras allows various vision tasks to be processed in a distributed fashion. While cameras may have different tasks, we concentrate on distributed tracking in smart camera networks. This application introduces various highly interesting problems. Firstly, how can conflicting goals be satisfied such as cameras in the network try to track objects while also trying to keep communication overhead low? Secondly, how can cameras in the network self adapt in response to the behavior of objects and changes in scenarios, to ensure continued efficient performance? Thirdly, how can cameras organise themselves to improve the overall network's performance and efficiency? This paper presents a simulation environment, called CamSim, allowing distributed self-adaptation and self-organisation algorithms to be tested, without setting up a physical smart camera network. The simulation tool is written in Java and hence allows high portability between different operating systems. Relaxing various problems of computer vision and network communication enables a focus on implementing and testing new self-adaptation and self-organisation algorithms for cameras to use.

Learning to be different:heterogeneity and efficiency in distributed smart camera networks

In this paper we study the self-organising behaviour of smart camera networks which use market-based handover of object tracking responsibilities to achieve an efficient allocation of objects to cameras. Specifically, we compare previously known homogeneous configurations, when all cameras use the same marketing strategy, with heterogeneous configurations, when each camera makes use of its own, possibly different marketing strategy. Our first contribution is to establish that such heterogeneity of marketing strategies can lead to system wide outcomes which are Pareto superior when compared to those possible in homogeneous configurations. However, since the particular configuration required to lead to Pareto efficiency in a given scenario will not be known in advance, our second contribution is to show how online learning of marketing strategies at the individual camera level can lead to high performing heterogeneous configurations from the system point of view, extending the Pareto front when compared to the homogeneous case. Our third contribution is to show that in many cases, the dynamic behaviour resulting from online learning leads to global outcomes which extend the Pareto front even when compared to static heterogeneous configurations. Our evaluation considers results obtained from an open source simulation package as well as data from a network of real cameras. © 2013 IEEE.

Resource-aware configuration in smart camera networks

A recent trend in smart camera networks is that they are able to modify the functionality during runtime to better reflect changes in the observed scenes and in the specified monitoring tasks. In this paper we focus on different configuration methods for such networks. A configuration is given by three components: (i) a description of the camera nodes, (ii) a specification of the area of interest by means of observation points and the associated monitoring activities, and (iii) a description of the analysis tasks. We introduce centralized, distributed and proprioceptive configuration methods and compare their properties and performance. © 2012 IEEE.

Static, dynamic, and adaptive heterogeneity in distributed smart camera networks

We study heterogeneity among nodes in self-organizing smart camera networks, which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when cameras use the same strategy, with heterogeneous configurations, when cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes that are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: nonadaptive mixed strategies and adaptive strategies, which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralized online learning can lead to more efficient outcomes than the homogeneous case. In some cases, outcomes from online learning were more efficient than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic, and adaptive heterogeneity, suggesting that all have a key role in achieving efficient self-organization.

Self-aware and self-expressive camera networks

Smart cameras perform on-board image analysis, adapt their algorithms to changes in their environment, and collaborate with other networked cameras to analyze the dynamic behavior of objects. A proposed computational framework adopts the concepts of self-awareness and self-expression to more efficiently manage the complex tradeoffs among performance, flexibility, resources, and reliability. The Web extra at http://youtu.be/NKe31-OKLz4 is a video demonstrating CamSim, a smart camera simulation tool, enables users to test self-adaptive and self-organizing smart-camera techniques without deploying a smart-camera network.

Interpretational variability of structural traps : implications for exploration risk and volume uncertainty

We would like to acknowledge and thank Frank Peel and Seb Turner for taking time to review this paper. Also thanks to Marguerite Fleming for perpetual encouragement. From: Richards, F. L., Richardson, N. J., Rippington, S. J., Wilson, R. W. & Bond, C. E. (eds) 2015. Industrial Structural Geology: Principles, Techniques and Integration. Geological Society, London,

Interpretational variability of structural traps : implications for exploration risk and volume uncertainty

We would like to acknowledge and thank Frank Peel and Seb Turner for taking time to review this paper. Also thanks to Marguerite Fleming for perpetual encouragement. From: Richards, F. L., Richardson, N. J., Rippington, S. J., Wilson, R. W. & Bond, C. E. (eds) 2015. Industrial Structural Geology: Principles, Techniques and Integration. Geological Society, London,

Camera woman and sound technician at NOW conference in Houston

General note: Title and date provided by Bettye Lane.

Victor Gautbaum speaking (back to camera) at City Hall for Intro 475

General note: Title and date provided by Bettye Lane.

Learning and Exploiting Camera Geometry for Computer Vision

This work explores the use of statistical methods in describing and estimating camera poses, as well as the information feedback loop between camera pose and object detection. Surging development in robotics and computer vision has pushed the need for algorithms that infer, understand, and utilize information about the position and orientation of the sensor platforms when observing and/or interacting with their environment.

The first contribution of this thesis is the development of a set of statistical tools for representing and estimating the uncertainty in object poses. A distribution for representing the joint uncertainty over multiple object positions and orientations is described, called the mirrored normal-Bingham distribution. This distribution generalizes both the normal distribution in Euclidean space, and the Bingham distribution on the unit hypersphere. It is shown to inherit many of the convenient properties of these special cases: it is the maximum-entropy distribution with fixed second moment, and there is a generalized Laplace approximation whose result is the mirrored normal-Bingham distribution. This distribution and approximation method are demonstrated by deriving the analytical approximation to the wrapped-normal distribution. Further, it is shown how these tools can be used to represent the uncertainty in the result of a bundle adjustment problem.

Another application of these methods is illustrated as part of a novel camera pose estimation algorithm based on object detections. The autocalibration task is formulated as a bundle adjustment problem using prior distributions over the 3D points to enforce the objects' structure and their relationship with the scene geometry. This framework is very flexible and enables the use of off-the-shelf computational tools to solve specialized autocalibration problems. Its performance is evaluated using a pedestrian detector to provide head and foot location observations, and it proves much faster and potentially more accurate than existing methods.

Finally, the information feedback loop between object detection and camera pose estimation is closed by utilizing camera pose information to improve object detection in scenarios with significant perspective warping. Methods are presented that allow the inverse perspective mapping traditionally applied to images to be applied instead to features computed from those images. For the special case of HOG-like features, which are used by many modern object detection systems, these methods are shown to provide substantial performance benefits over unadapted detectors while achieving real-time frame rates, orders of magnitude faster than comparable image warping methods.

The statistical tools and algorithms presented here are especially promising for mobile cameras, providing the ability to autocalibrate and adapt to the camera pose in real time. In addition, these methods have wide-ranging potential applications in diverse areas of computer vision, robotics, and imaging.

Defining the Role of Host Cell Chromatin Traps in Chlamydia trachomatis Pathogenesis

Chlamydia trachomatis (CT) is the most common bacterial agent of sexually transmitted infection and can cause damaging inflammation of the female reproductive tract. As an obligate intracellular pathogen, CT must exit exhausted host cells in a manner that favors successful dissemination. Epithelial cells infected with CT expel decondensed nuclear chromatin at the conclusion of an infectious cycle, and these ensnare CT particles. Whether these chromatin traps benefit the host or the pathogen is not obvious. The overall goal of this work is to begin discerning between these possibilities by determining how chromatin traps impact CT survival following exit and how traps contribute to CT-induced inflammatory processes.

Two devices, each of which measure water velocity; on the right is an acoustic device that is permanently maintained at Gumbo Limbo by PI Rene Price; on the left is a 3D camera with a long fiberoptic lens, deployed for testing purposes by collaborator Evan Variano.

http://digitalcommons.fiu.edu/fce_lter_photos/1303/thumbnail.jpg

Field trip to SRS-5 during Caribbean Workshop- Litter traps at SRS-5 (Robert Twilley foreground)

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Particle flux in the NE Atlantic as recorded by sediment traps

Downward particle flux was measured using sediment traps at various depths over the Porcupine Abyssal Plain (water depth ab. 4850 m) for prolonged periods from 1989 to 1999. A strong seasonal pattern of flux was evident reaching a maximum in mid-summer. The composition of the material changed with depth, reflecting the processes of remineralisation and dissolution as the material sank through the water column. However, there was surprisingly little seasonal variation in its composition to reflect changes in the biology of the euphotic zone. Currents at the site have a strong tidal component with speeds almost always less than 15 cm/sec. In the deeper part of the water column they tend to be northerly in direction, when averaged over periods of several months. A model of upper ocean biogeochemistry forced by meteorology was run for the decade in order to provide an estimate of flux at 3000 m depth. Agreement with measured organic carbon flux is good, both in terms of the timings of the annual peaks and in the integrated annual flux. Interannual variations in the integrated flux are of similar magnitude for both the model output and sediment trap measurements, but there is no significant relationship between these two sets of estimates. No long-term trend in flux is evident, either from the model, or from the measurements. During two spring/summer periods, the marine snow concentration in the water column was assessed by time-lapse photography and showed a strong peak at the start of the downward pulse of material at 3000 m. This emphasises the importance of large particles during periods of maximum flux and at the start of flux peaks. Time lapse photographs of the seabed show a seasonal cycle of coverage of phytodetrital material, in agreement with the model output both in terms of timing and magnitude of coverage prior to 1996. However, after a change in the structure of the benthic community in 1996 no phytodetritus was evident on the seabed. The model output shows only a single peak in flux each year, whereas the measured data usually indicated a double peak. It is concluded that the observed double peak may be a reflection of lowered sediment trap efficiency when flux is very high and is dominated by large marine snow particles. Resuspension into the trap 100 m above the seabed, when compared to the primary flux at 3000 m depth (1800 mab) was lower during periods of high primary flux probably because of a reduction in the height of resuspension when the material is fresh. At 2 mab, the picture is more complex with resuspension being enhanced during the periods of higher flux in 1997, which is consistent with this hypothesis. However there was rather little relationship to flux at 3000 m in 1998. At 3000 m depth, the Flux Stability Index (FSI), which provides a measure of the constancy of the seasonal cycle of flux, exhibited an inverse relationship with flux, such that the highest flux of organic carbon was recorded during the year with the greatest seasonal variation.