Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy

In this paper we shall consider the problem of deploying attention to subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer's attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream. The approach proposed here is suitable to be exploited for multi-stream video summarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g. activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Dataset, a publicly available dataset, are presented to illustrate the utility of the proposed technique.Comment: Accepted to IEEE Transactions on Image Processin

An MPEG-7 scheme for semantic content modelling and filtering of digital video

Abstract Part 5 of the MPEG-7 standard specifies Multimedia Description Schemes (MDS); that is, the format multimedia content models should conform to in order to ensure interoperability across multiple platforms and applications. However, the standard does not specify how the content or the associated model may be filtered. This paper proposes an MPEG-7 scheme which can be deployed for digital video content modelling and filtering. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user. We present details of the scheme, front-end systems used for content modelling and filtering and experiences with a number of users

A census of young stellar populations in the warm ULIRG PKS1345+12

We present a detailed investigation of the young stellar populations(YSP) in the radio-loud ultra luminous infrared galaxy (ULIRG) PKS1345+12, based on high resolution HST imaging and long slit spectra taken with the WHT. While the images clearly show bright knots suggestive of super star clusters(SSC), the spectra reveal the presence of YSP in the diffuse light across the full extent of the halo of the merging-double nucleus system. Spectral synthesis modelling has been used to estimate the ages of the YSP for both the SSC and the diffuse light sampled by the spectra. For the SSC we find ages t{SSC} < 6 Myr with reddenings 0.2 < E(B-V) < 0.5 and masses 10e6 < M{SSC} < 10e7 M{solar}. However, in some regions of the galaxy we find that the spectra of the diffuse light component can only be modelled with a relatively old post-starburst YSP (0.04 - 1.0 Gyr) or with a disk galaxy template spectrum. The results demonstrate the importance of accounting for reddening in photometric studies of SSC, and highlight the dangers of focussing on the highest surface brightness regions when trying to obtain a general impression of the star formation activity in the host galaxies of ULIRGs. The case of PKS1345+12 provides clear evidence that the star formation histories of the YSP in ULIRGs are complex. Intriguingly, our long-slit spectra show line splitting at the locations of the SSC, indicating that they are moving at up to 450km s-1 with respect to the local ambient gas. Given their kinematics, it is plausible that the SSC have been formed either in fast moving gas streams/tidal tails that are falling back into the nuclear regions as part of the merger process, or as a consequence of jet-induced star formation linked to the extended, diffuse radio emission detected in the halo of the galaxyComment: accepted for publication in MNRA

Evaluation of GPU/CPU Co-Processing Models for JPEG 2000 Packetization

With the bottom-line goal of increasing the throughput of a GPU-accelerated JPEG 2000 encoder, this paper evaluates whether the post-compression rate control and packetization routines should be carried out on the CPU or on the GPU. Three co-processing models that differ in how the workload is split among the CPU and GPU are introduced. Both routines are discussed and algorithms for executing them in parallel are presented. Experimental results for compressing a detail-rich UHD sequence to 4 bits/sample indicate speed-ups of 200x for the rate control and 100x for the packetization compared to the single-threaded implementation in the commercial Kakadu library. These two routines executed on the CPU take 4x as long as all remaining coding steps on the GPU and therefore present a bottleneck. Even if the CPU bottleneck could be avoided with multi-threading, it is still beneficial to execute all coding steps on the GPU as this minimizes the required device-to-host transfer and thereby speeds up the critical path from 17.2 fps to 19.5 fps for 4 bits/sample and to 22.4 fps for 0.16 bits/sample

Autonomous real-time surveillance system with distributed IP cameras

An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator

Next challenges for adaptive learning systems

Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.</jats:p

Modelling the Galaxy in the era of Gaia

The body of photometric and astrometric data on stars in the Galaxy has been growing very fast in recent years (Hipparcos/Tycho, OGLE-3, 2-Mass, DENIS, UCAC2, SDSS, RAVE, Pan Starrs, Hermes, ...) and in two years ESA will launch the Gaia satellite, which will measure astrometric data of unprecedented precision for a billion stars. On account of our position within the Galaxy and the complex observational biases that are built into most catalogues, dynamical models of the Galaxy are a prerequisite full exploitation of these catalogues. On account of the enormous detail in which we can observe the Galaxy, models of great sophistication are required. Moreover, in addition to models we require algorithms for observing them with the same errors and biases as occur in real observational programs, and statistical algorithms for determining the extent to which a model is compatible with a given body of data. JD5 reviewed the status of our knowledge of the Galaxy, the different ways in which we could model the Galaxy, and what will be required to extract our science goals from the data that will be on hand when the Gaia Catalogue becomes available.Comment: Proceedings of Joint Discussion 5 at IAU XXVII, Rio de Janeiro, August 2009; 31 page