Scalable software architecture for on-line multi-camera video processing

In this paper we present a scalable software architecture for on-line multi-camera video processing, that guarantees a good trade off between computational power, scalability and flexibility. The software system is modular and its main blocks are the Processing Units (PUs), and the Central Unit. The Central Unit works as a supervisor of the running PUs and each PU manages the acquisition phase and the processing phase. Furthermore, an approach to easily parallelize the desired processing application has been presented. In this paper, as case study, we apply the proposed software architecture to a multi-camera system in order to efficiently manage multiple 2D object detection modules in a real-time scenario. System performance has been evaluated under different load conditions such as number of cameras and image sizes. The results show that the software architecture scales well with the number of camera and can easily works with different image formats respecting the real time constraints. Moreover, the parallelization approach can be used in order to speed up the processing tasks with a low level of overhea

Adaptive Multi-Camera System for Real Time Object Detection

In this paper we present an adaptive multi-camera system for real time object detection able to efficiently adjust the computational requirements of video processing blocks to the available processing power and the activity of the scene. The system is based on a two level adaptation strategy that works at local and at global level. Object detection is based on a Gaussian mixtures model background subtraction algorithm. Results show that the system can efficiently adapt the algorithm parameters without a significant loss in the detection accuracy

A region based approach to background modeling in a wavelet multi-resolution framework

In the field of detection and monitoring of dynamic objects in quasi-static scenes, background subtraction techniques where background is modeled at pixel-level, although showing very significant limitations, are extensively used. In this work we propose a novel approach to background modeling that operates at region-level in a wavelet based multi-resolution framework. Based on a segmentation of the background, characterization is made for each region independently as a mixture of K Gaussian modes, considering the model of the approximation and detail coefficients at the different wavelet decomposition levels. Background region characterization is updated along time, and the detection of elements of interest is carried out computing the distance between background region models and those of each incoming image in the sequence. The inclusion of the context in the modeling scheme through each region characterization makes the model robust, being able to support not only gradual illumination and long-term changes, but also sudden illumination changes and the presence of strong shadows in the scen

Multiple dinosaur egg-shell occurrence in an Upper Cretaceous nesting site from Patagonia

The discovery of hundreds of megaloolithid-type egg-clutches (some including embryos of an indeterminate species of titanosaur sauropods) in several stratigraphical levels of the Late Cretaceous nesting site of Auca Mahuevo (Chiappe et al., 2005) unveiled important aspects of the reproductive behavior of sauropod dinosaurs and stimulated further work at other Patagonian dinosaur egg sites. In November of 2003, a join expedition of the National University of Comahue (Neuquén), the Museo Carmen Funes (Plaza Huincul) and the Museo de Lamarque (Lamarque) conducted fieldwork in several Late Cretaceous localities of Bajo Santa Rosa (center-north Río Negro Province, Argentina) bearing the remains of dinosaur eggs and other terrestrial vertebrates (figure 1). The main focus of this expedition was to assess the diversity of dinosaur eggs, to fine-tune the stratigraphy of the egg-bearing layers, and to document the spatial distribution of the egg-clutches. The overall results of this research were reported elsewhere (Salgado et al., 2007). In this contribution, we make focus in one of the fossil localities worked, Berthe IV, where the association of egg clutches likely belonging to different dinosaur species was detected (figure 1.2). Collected eggshells were observed with a binocular loupe ‘‘Stemi SV6 Zeiss’’, at magnifications of x1.0 and x3.2 (for macrocharacters). Microcharacters were observed in transverse thin section using a polarizing microscope (‘‘Zeiss Axioplan’’) at magnifications of x0.4 and x10. The eggshells were photographed using this polarizing microscope equipped with a digital camera. Prior to analysis, the eggshells were submitted to a process of cleaning using ultrasound (see Salgado et al., 2007 for further methodological details).Fil: Coria, Rodolfo Anibal. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Provincia del Neuquen. Municipalidad de Plaza Huincul. Museo "Carmen Funes"; ArgentinaFil: Salgado, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Museo de Geologia y Paleontologia; ArgentinaFil: Chiappe, Luis M.. Los Angeles County Museum of Natural History. Dinosaur Institute; Estados Unido

Simulation of the rainfall runoff process from a watershed by using a microcomputer

With the recent advent of the microcomputer, a powerful new tool is available for use by engineers and hydrologists. Some relevant characteristics of the microcomputers are the language used for programming (BASIC), its simplicity of operation, low cost, small size and easy transport to the office, laboratory or field for both data acquisition and model simulation;To accomplish the objectives of this research, the entire organization of the hydrologic part of the Kentucky Watershed Model (KWM) was restructured to fit the operational characteristics of an Apple II-Plus microcomputer with 48 K bytes of memory capacity. The resulting computer model (Apple-KWM) consists of a single program and 7 text files containing input/output data. The program permits the user to run it on a daily or yearly basis;The effectiveness of the model was tested using 1976, 1977, 1979 and 1980 water year data from Four Mile Creek Watershed, near Traer, Iowa. The results were compared with those obtained by using the original program written in FORTRAN. From the results of this study, it appears that the efficiency and precision of a hydrologic simulation process when using a microcomputer are as good as those from using large computer systems. The current Applesoft version of the KWM gives essentially the same simulation results as the original FORTRAN version when applied to Four Mile Creek Watershed. Small differences were observed which can be attributed to round-off error of the microcomputer;The Apple-KWM program requires about 16 K bytes of memory. On a yearly basis computation, 1.2 K bytes are needed for variables, 8.7 K for arrays and only 3 bytes for strings. About 10 K are still available for further modifications and/or additions. By using the compiler program TASC (Microsoft Inc.) computation time was reduced to 40 minutes as compared to its Applesoft counterpart which takes 5.5 hours for a full year computation;The daily basis option of the program permits the user to calculate the streamflows from a watershed on a day-by-day basis. This alternative gives the designer up-to-date information of the runoff process which can be used advantageously in forecasting flood discharges

Aproximación a la zonificación del CBR en la localidad de Chapinero

Con la ayuda de diferentes estudios de suelos realizados en la ciudad de Bogotá entre los años 2006 a 2009 efectuados para construir edificaciones, se ha propuesto utilizar los mismos para llevar a cabo una aproximación al parámetro inicial empleado en el diseños de pavimentos, bien sea de tipo flexible o rígido, dicho parámetro es el CBR (California Bearing Ratio), derivado de sus siglas en inglés Relación de Soporte California, a causa de la ausencia de una herramienta que permita efectuar pre dimensionamientos que permitan obtener presupuestos para obras en la materia descrita

Vehicle detection and tracking using homography-based plane rectification and particle filtering

This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results

Multiple object tracking using an automatic veriable-dimension particle filter

Object tracking through particle filtering has been widely addressed in recent years. However, most works assume a constant number of objects or utilize an external detector that monitors the entry or exit of objects in the scene. In this work, a novel tracking method based on particle filtering that is able to automatically track a variable number of objects is presented. As opposed to classical prior data assignment approaches, adaptation of tracks to the measurements is managed globally. Additionally, the designed particle filter is able to generate hypotheses on the presence of new objects in the scene, and to confirm or dismiss them by gradually adapting to the global observation. The method is especially suited for environments where traditional object detectors render noisy measurements and frequent artifacts, such as that given by a camera mounted on a vehicle, where it is proven to yield excellent results

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences