Chondrodystrophic dwarfism and multiple malformations in two sisters.

A genetic skeletal dysplasia with dwarfism, scoliosis and multiple skeletal defects was observed in two sisters. Only nine cases with similar features have been reported in the literature

Multiple Vehicle Detection and Tracking in Hard Real Time

A vision system has been developed that recognizes and tracks multiple vehicles from sequences of gray-scale images taken from a moving car in hard real-time. Recognition is accomplished by combining the analysis of single image frames with the analysis of the motion information provided by multiple consecutive image frames. In single image frames, cars are recognized by matching deformable gray-scale templates, by detecting image features, such as corners, and by evaluating how these features relate to each other. Cars are also recognized by differencing consecutive image frames and by tracking motion parameters that are typical for cars. The vision system utilizes the hard real-time operating system Maruti which guarantees that the timing constraints on the various processes of the vision system are satisfied. The dynamic creation and termination of tracking processes optimizes the amount of computational resources spent and allows fast detection and tracking of multiple cars. Experimental results demonstrate robust, real-time recognition and tracking over thousands of image frames. (Also cross-referenced as UMIACS-TR-96-52

Energy efficiency improvements through surveillance applications in industrial buildings

Presence sensors for energy control based on classic technologies to detect movement are now commonly seen in many areas of life. However, their use in structurally complex environments is not very common, due to their lack of reliability in these types of situations. Falling prices in technologies associated with surveillance applications are leading to a huge increase in their use in all types of environment, with monitoring of traffic and people the most common of these. In this work, we carry out an analysis of occupancy patterns in manufacturing industries with the aim of determining the possible energy savings that could be obtained using these new technologies. We also carry out an analysis of the possibilities of using these technologies as presence sensors, analyzing the trends and limitations associated with them. © 2011 Elsevier B.V.This work is supported by the MCYT of Spain under the project TIN2010-21378-C02-02.Silvestre-Blanes, J.; Pérez Llorens, R. (2011). Energy efficiency improvements through surveillance applications in industrial buildings. Energy and Buildings. 43(6):1334-1340. https://doi.org/10.1016/j.enbuild.2011.01.017S1334134043

Improving the Segmentation Stage of a Pedestrian Tracking Video-based System by means of Evolution Strategies

12 pages, 7 figures.-- Contributed to: Eighth European Workshop on Evolutionary Computation in Image Analysis and Signal Processing (EvoIASP 2006, Budapest, Hungary, Apr 10-12, 2006).Pedestrian tracking video-based systems present particular problems such as the multi fragmentation or low level of compactness of the resultant blobs due to the human shape or movements. This paper shows how to improve the segmentation stage of a video surveillance system by adding morphological post-processing operations so that the subsequent blocks increase their performance. The adjustment of the parameters that regulate the new morphological processes is tuned by means of Evolution Strategies. Finally, the paper proposes a group of metrics to assess the global performance of the surveillance system. After the evaluation over a high number of video sequences, the results show that the shape of the tracks match up more accurately with the parts of interests. Thus, the improvement of segmentation stage facilitates the subsequent stages so that global performance of the surveillance system increases.Funded by CICYT (TIC2002-04491-C02-02)Publicad

Detecting Carried Objects in Short Video Sequences

Abstract. We propose a new method for detecting objects such as bags carried by pedestrians depicted in short video sequences. In common with earlier work [1, 2] on the same problem, the method starts by averaging aligned foreground regions of a walking pedestrian to produce a rep-resentation of motion and shape (known as a temporal template) that has some immunity to noise in foreground segmentations and phase of the walking cycle. Our key novelty is for carried objects to be revealed by comparing the temporal templates against view-specific exemplars generated offline for unencumbered pedestrians. A likelihood map ob-tained from this match is combined in a Markov random field with a map of prior probabilities for carried objects and a spatial continuity as-sumption, from which we obtain a segmentation of carried objects using the MAP solution. We have re-implemented the earlier state of the art method [1] and demonstrate a substantial improvement in performance for the new method on the challenging PETS2006 dataset [3]. Although developed for a specific problem, the method could be applied to the de-tection of irregularities in appearance for other categories of object that move in a periodic fashion.

High resolution dynamical mapping of social interactions with active RFID

In this paper we present an experimental framework to gather data on face-to-face social interactions between individuals, with a high spatial and temporal resolution. We use active Radio Frequency Identification (RFID) devices that assess contacts with one another by exchanging low-power radio packets. When individuals wear the beacons as a badge, a persistent radio contact between the RFID devices can be used as a proxy for a social interaction between individuals. We present the results of a pilot study recently performed during a conference, and a subsequent preliminary data analysis, that provides an assessment of our method and highlights its versatility and applicability in many areas concerned with human dynamics

A vision-based system for intelligent monitoring: human behaviour analysis and privacy by context

Due to progress and demographic change, society is facing a crucial challenge related to increased life expectancy and a higher number of people in situations of dependency. As a consequence, there exists a significant demand for support systems for personal autonomy. This article outlines the vision@home project, whose goal is to extend independent living at home for elderly and impaired people, providing care and safety services by means of vision-based monitoring. Different kinds of ambient-assisted living services are supported, from the detection of home accidents, to telecare services. In this contribution, the specification of the system is presented, and novel contributions are made regarding human behaviour analysis and privacy protection. By means of a multi-view setup of cameras, people's behaviour is recognised based on human action recognition. For this purpose, a weighted feature fusion scheme is proposed to learn from multiple views. In order to protect the right to privacy of the inhabitants when a remote connection occurs, a privacy-by-context method is proposed. The experimental results of the behaviour recognition method show an outstanding performance, as well as support for multi-view scenarios and real-time execution, which are required in order to provide the proposed services

Patch-Based Experiments with Object Classification in Video Surveillance

We present a patch-based algorithm for the purpose of object classification in video surveillance. Within detected regions-of-interest (ROIs) of moving objects in the scene, a feature vector is calculated based on template matching of a large set of image patches. Instead of matching direct image pixels, we use Gabor-filtered versions of the input image at several scales. This approach has been adopted from recent experiments in generic object-recognition tasks. We present results for a new typical video surveillance dataset containing over 9,000 object images. Furthermore, we compare our system performance with another existing smaller surveillance dataset. We have found that with 50 training samples or higher, our detection rate is on the average above 95%. Because of the inherent scalability of the algorithm, an embedded system implementation is well within reach